Results for 'Perceptron'

51 found
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  1. Perceptron Connectives in Knowledge Representation.Pietro Galliani, Guendalina Righetti, Daniele Porello, Oliver Kutz & Nicolas Toquard - 2020 - In Pietro Galliani, Guendalina Righetti, Daniele Porello, Oliver Kutz & Nicolas Toquard, Knowledge Engineering and Knowledge Management - 22nd International Conference, {EKAW} 2020, Bolzano, Italy, September 16-20, 2020, Proceedings. Lecture Notes in Computer Science 12387. pp. 183-193.
    We discuss the role of perceptron (or threshold) connectives in the context of Description Logic, and in particular their possible use as a bridge between statistical learning of models from data and logical reasoning over knowledge bases. We prove that such connectives can be added to the language of most forms of Description Logic without increasing the complexity of the corresponding inference problem. We show, with a practical example over the Gene Ontology, how even simple instances of perceptron (...)
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  2.  56
    Perceptron versus automaton in the finitely repeated prisoner’s dilemma.Sylvain Béal - 2010 - Theory and Decision 69 (2):183-204.
    We study the finitely repeated prisoner’s dilemma in which the players are restricted to choosing strategies which are implementable by a machine with a bound on its complexity. One player has to use a finite automaton while the other player has to use a finite perceptron. Some examples illustrate that the sets of strategies which are induced by these two types of machines are different and not ordered by set inclusion. Repeated game payoffs are evaluated according to the limit (...)
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  3. The perceptron: A probabilistic model for information storage and organization in the brain.F. Rosenblatt - 1958 - Psychological Review 65 (6):386-408.
    If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the.
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  4.  41
    Using perceptrons to explore the reorientation task.Michael R. W. Dawson, Debbie M. Kelly, Marcia L. Spetch & Brian Dupuis - 2010 - Cognition 114 (2):207-226.
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  5.  13
    The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant.J. Kivinen, M. K. Warmuth & P. Auer - 1997 - Artificial Intelligence 97 (1-2):325-343.
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  6.  21
    Hybrid Modelling of Multilayer Perceptron Ensembles for Predicting the Response of Bolted Lap Joints.J. Fernandez-Ceniceros, F. Antonanzas-Torres, F. J. Martinez-De-Pison & A. Sanz-Garcia - 2015 - Logic Journal of the IGPL 23 (3):451-462.
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  7.  12
    Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization.Balasundram P. Amavasai & P. I. Rockett - 2008 - Journal of Intelligent Systems 17 (Supplement):57-86.
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  8.  55
    The dynamical model is a perceptron.Bruce Bridgeman - 1998 - Behavioral and Brain Sciences 21 (5):631-632.
    Van Gelder's example of a dynamical model is a Perceptron. The similarity of dynamical models and Perceptrons in turn exemplifies the close relationship between dynamical and algorithmic models. Both are models, not literal descriptions of brains. The brain states of standard modeling are better conceived as processes in the dynamical sense, but algorithmic models remain useful.
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  9.  11
    VC-dimension of a context-dependent perceptron.Piotr Ciskowski - 2001 - In P. Bouquet V. Akman, Modeling and Using Context. Springer. pp. 429--432.
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  10.  13
    Growing methods for constructing recursive deterministic perceptron neural networks and knowledge extraction.M. Tajine & D. Elizondo - 1998 - Artificial Intelligence 102 (2):295-322.
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  11.  42
    A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron.Nelson M. Maldonato, Raffaele Sperandeo, Enrico Moretto & Silvia Dell'Orco - 2018 - Frontiers in Psychology 9.
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  12.  43
    Clinical Diagnosis of Creutzfeldt-Jakob Disease Using a Multi-Layer Perceptron Neural Network Classifier.Κ Sutherland, R. De Silva & R. G. Will - 1997 - Journal of Intelligent Systems 7 (1-2):1-18.
  13. We've been here before: AI promised human-like machines – in 1958.Danielle Williams - 2024 - The Conversation.
    A roomsize computer equipped with a new type of circuitry, the Perceptron, was introduced to the world in 1958 in a brief news story buried deep in The New York Times. The story cited the U.S. Navy as saying that the Perceptron would lead to machines that “will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.” More than six decades later, similar claims are being made about current artificial intelligence. So, what’s (...)
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  14. Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello, Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to (...)
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  15.  23
    The Algebraic Mind: Integrating Connectionism and Cognitive Science.Gary F. Marcus - 2001 - MIT Press.
    1 Cognitive Architectures 2 Multilayer Perceptrons 3 Relations between Variables 4 Structured Representations 5 Individuals 6 Where does the Machinery of Symbol Manipulation Come From? 7 Conclusions.
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  16. Predicting Tumor Category Using Artificial Neural Networks.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (2):1-7.
    In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained (...)
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  17.  68
    Connectionist Models and Linguistic Theory: Investigations of Stress Systems in Language.Prahlad Gupta & David S. Touretzky - 1994 - Cognitive Science 18 (1):1-50.
    We question the widespread assumption that linguistic theory should guide the formulation of mechanistic accounts of human language processing. We develop a pseudo‐linguistic theory for the domain of linguistic stress, based on observation of the learning behavior of a perceptron exposed to a variety of stress patterns. There are significant similarities between our analysis of perception stress learning and metrical phonology, the linguistic theory of human stress. Both approaches attempt to identify salient characteristics of the stress systems under examination (...)
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  18.  88
    On estimation of functional causal models : general results and application to the post-nonlinear causal model.Kun Zhang, Zhikun Wang, Jiji Zhang & Bernhard Scholkopf - unknown
    Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyvärinen 2009b]. Functional causal models represent the effect as a function of the direct causes together with an independent noise term. Examples include the linear non-Gaussian acyclic model, nonlinear additive noise model, and post-nonlinear model. Currently, there are two ways to estimate the parameters in the models: dependence (...)
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  19.  41
    A comparison of connectionist models of music recognition and human performance.Catherine Stevens & Cyril Latimer - 1992 - Minds and Machines 2 (4):379-400.
    Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from (...)
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  20. Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model.Ángel F. Villarejo-Ramos, Juan-Pedro Cabrera-Sánchez, Juan Lara-Rubio & Francisco Liébana-Cabanillas - 2021 - Frontiers in Psychology 12:651398.
    The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with (...)
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  21.  32
    Grounding as a Side‐Effect of Grounding.Staffan Larsson - 2018 - Topics in Cognitive Science 10 (2):389-408.
    In relation to semantics, “grounding” has two relevant meanings. “Symbol grounding” is the process of connecting symbols to perception and the world. “Communicative grounding” is the process of interactively adding to common ground in dialog. Strategies for grounding in human communication include, crucially, strategies for resolving troubles caused by various kinds of miscommunication. As it happens, these two processes of grounding are closely related. As a side-effect of grounding an utterance, dialog participants may adjust the meanings they assign to linguistic (...)
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  22.  25
    An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning.S. M. Taslim Uddin Raju, Amlan Sarker, Apurba Das, Md Milon Islam, Mabrook S. Al-Rakhami, Atif M. Al-Amri, Tasniah Mohiuddin & Fahad R. Albogamy - 2022 - Complexity 2022:1-19.
    This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging ), boosting and extreme gradient boosting regression ), and stacking are employed as ensemble models. Different machine learning approaches, including support vector regression, extreme learning machine, and multilayer perceptron neural network, are adopted as reference models. In order to maximize the determination coefficient value and reduce the root mean square error, hyperparameters are set (...)
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  23.  18
    Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH.Pouya Aghelpour, Hadigheh Bahrami-Pichaghchi & Farzaneh Karimpour - 2022 - Complexity 2022:1-18.
    Evapotranspiration represents the water requirement of plants during their growing season, and its accurate measurement at the farm is essential for agricultural water planners and managers. Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration measurement by machine learning techniques and the least available climatic variables. For this (...)
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  24.  20
    A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction.Jordan J. Bird, Diego R. Faria, Luis J. Manso, Anikó Ekárt & Christopher D. Buckingham - 2019 - Complexity 2019:1-14.
    This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long (...)
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  25.  14
    Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning.Nihad Brahimi, Huaping Zhang, Lin Dai & Jianzi Zhang - 2022 - Complexity 2022:1-20.
    The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer (...)
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  26.  33
    An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries.Zhang Peng, Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh, Dai Yonghong, Ihsan Ullah & Farid Ullah - 2021 - Complexity 2021:1-10.
    The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron, recurrent neural network, and autoregressive integrated moving average on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three (...)
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  27.  15
    Time for a re-think: Problems with the parallel distributed approach to semantic cognition.Philip Quinlan - 2008 - Behavioral and Brain Sciences 31 (6):724-724.
    Rogers & McClelland (R&M) have provided an impressive outline of the capabilities of a class of multi-layered perceptrons that mimic many aspects of human knowledge acquisition. Despite this success, in the literature several basic issues are raised and concerns are expressed. Indeed, the problems are so acute that a different way of thinking is called for. In this commentary it is suggested that rational models approach provides a promising alternative.
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  28.  23
    A Stock Closing Price Prediction Model Based on CNN-BiSLSTM.Haiyao Wang, Jianxuan Wang, Lihui Cao, Yifan Li, Qiuhong Sun & Jingyang Wang - 2021 - Complexity 2021:1-12.
    As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory improved on bidirectional long short-term memory adds (...)
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  29. Discourseology of Linguistic Consciousness: Neural Network Modeling of Some Structural and Semantic Relationships.Vitalii Shymko - 2021 - Psycholinguistics 29 (1):193-207.
    Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess the (...)
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  30. Design principles and mechanistic explanation.Wei Fang - 2022 - History and Philosophy of the Life Sciences 44 (4):1-23.
    In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, (...)
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  31.  21
    Why is neuron modeling of particular philosophical interest?Paweł Polak - 2022 - Zagadnienia Filozoficzne W Nauce 73:347-356.
    This review article discusses Andrzej Bielecki’s book _Models of Neurons and Perceptrons: Selected Problems and Challenges_, as published by Springer International Publishing. This work exemplifies “philosophy in science” by adopting a broad, multidisciplinary perspective for the issues related to the simulation of neurons and neural networks, and the author has addressed many of the important philosophical assumptions that are entangled in this area of modeling. Bielecki also raises several important methodological issues about modeling. This book is recommended for any philosophers (...)
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  32.  22
    Homophily-Based Link Prediction in The Facebook Online Social Network: A Rough Sets Approach.Roa A. Aboo Khachfeh & Islam Elkabani - 2015 - Journal of Intelligent Systems 24 (4):491-503.
    Online social networks are highly dynamic and sparse. One of the main problems in analyzing these networks is the problem of predicting the existence of links between users on these networks: the link prediction problem. Many studies have been conducted to predict links using a variety of techniques like the decision tree and the logistic regression approaches. In this work, we will illustrate the use of the rough set theory in predicting links over the Facebook social network based on homophilic (...)
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  33.  13
    Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset.Xuhui Song & Linyuan Fan - 2021 - Complexity 2021:1-10.
    In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are used (...)
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  34.  2
    (1 other version)Why don’t transformers think like humans?А. Б Хомяков - 2025 - Philosophical Problems of IT and Cyberspace (PhilIT&C) 2:87-98.
    Large language models in the form of chatbots very realistically imitate a dialogue as an omniscient interlocutor and therefore have become widespread. But even Google in its Gemini chatbot does not recommend trusting what the chatbot will write and asks to check its answers. In this review, various types of LLM errors such as the curse of inversion, number processing, etc. will be analyzed to identify their causes. Such an analysis led to the conclusion about the common causes of all (...)
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  35.  14
    A comparative study of neural network architectures for software vulnerability forecasting.Ovidiu Cosma, Petrică C. Pop & Laura Cosma - forthcoming - Logic Journal of the IGPL.
    The frequency of cyberattacks has been rapidly increasing in recent times, which is a significant concern. These attacks exploit vulnerabilities present in the software components that constitute the targeted system. Consequently, the number of vulnerabilities within these software components serves as an indicator of the system’s level of security and trustworthiness. This paper compares the accuracy, trainability and stability to configuration parameters of several neural network architectures, namely Long Short-Term Memory, Multilayer Perceptron and Convolutional Neural Network. These architectures are (...)
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  36.  20
    LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks.Yunliang Chen, Shaoqian Chen, Nian Zhang, Hao Liu, Honglei Jing & Geyong Min - 2021 - Complexity 2021:1-10.
    The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status (...)
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  37.  23
    Data streams classification using deep learning under different speeds and drifts.Pedro Lara-Benítez, Manuel Carranza-García, David Gutiérrez-Avilés & José C. Riquelme - 2023 - Logic Journal of the IGPL 31 (4):688-700.
    Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows (...)
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  38.  21
    Artificial Intelligence-Based Real-Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks.Yu Li & Peihua Liu - 2022 - Complexity 2022:1-8.
    Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. (...)
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  39.  23
    An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection.C. Manzano, C. Meneses, P. Leger & H. Fukuda - 2022 - Complexity 2022:1-18.
    Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural (...)
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  40. The Exploratory Status of Postconnectionist Models.Miljana Milojevic & Vanja Subotić - 2020 - Theoria: Beograd 2 (63):135-164.
    This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...)
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  41.  57
    The Philosophic Foundations of Mimetic Theory and Cognitive Science: (Including Artificial Intelligence).Jean-Pierre Dupuy - 2022 - Contagion: Journal of Violence, Mimesis, and Culture 29 (1):1-13.
    In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I feel fully (...)
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  42.  12
    DGGCCM: a hybrid neural model for legal event detection.Shutao Gong & Xudong Luo - forthcoming - Artificial Intelligence and Law:1-41.
    This paper introduces an advanced event detection model for legal intelligence, focusing on identifying event types in legal cases by examining trigger word candidates. It employs the DeBERTa pre-trained language model for encoding sentences into enriched word representations, supplemented by the Global Pointer neural network for initial scoring. The model further uses a graph convolutional network, conditional layer normalisation, and a convolutional neural network to extract features from these representations. A multilayer perceptron then determines the event type based on (...)
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  43.  79
    Ensemble Machine Learning Model for Classification of Spam Product Reviews.Muhammad Fayaz, Atif Khan, Javid Ur Rahman, Abdullah Alharbi, M. Irfan Uddin & Bader Alouffi - 2020 - Complexity 2020:1-10.
    Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam (...)
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  44.  9
    The application of artificial neural networks to forecast financial time series.D. González-Cortés, E. Onieva, I. Pastor & J. Wu - forthcoming - Logic Journal of the IGPL.
    The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, (...)
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  45.  50
    Compensating atmospheric turbulence with CNNs for defocused pupil image wavefront sensors.Sergio Luis Suárez Gómez, Carlos González-Gutiérrez, Juan Díaz Suárez, Juan José Fernández Valdivia, José Manuel Rodríguez Ramos, Luis Fernando Rodríguez Ramos & Jesús Daniel Santos Rodríguez - 2021 - Logic Journal of the IGPL 29 (2):180-192.
    Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work, computational results are presented for a modified curvature sensor, the tomographic pupil image wavefront sensor, which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional neural networks are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely (...)
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  46.  19
    Student Performance Prediction with Optimum Multilabel Ensemble Model.Abrahaley Teklay Haile & Ephrem Admasu Yekun - 2021 - Journal of Intelligent Systems 30 (1):511-523.
    One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as (...)
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  47.  17
    Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm.Yukai Hao & Xin Qiu - 2021 - Complexity 2021:1-10.
    In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network is proposed for NLOS positioning error caused by non-line-of-sight propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training (...)
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  48.  23
    Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques.Marium Mehmood, Nasser Alshammari, Saad Awadh Alanazi & Fahad Ahmad - 2022 - Complexity 2022:1-11.
    The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment (...)
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  49.  29
    Constrained connectionism and the limits of human semantics: A review essay of Terry regier's the human semantic potential. [REVIEW]Robert M. French - 1999 - Philosophical Psychology 12 (4):515 – 523.
    Taking to heart Massaro's [(1988) Some criticisms of connectionist models of human performance, Journal of Memory and Language, 27, 213-234] criticism that multi-layer perceptrons are not appropriate for modeling human cognition because they are too powerful (i.e. they can simulate just about anything, which gives them little explanatory power), Regier develops the notion of constrained connectionism. The model that he discusses is a distributed network but with numerous constraints added that are (more or less) motivated by real psychophysical and neurophysical (...)
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  50.  19
    The learnability of natural concepts.Igor Douven - 2025 - Mind and Language 40 (1):120-135.
    According to a recent proposal, natural concepts are represented in an optimally designed similarity space, adhering to principles a skilled engineer would use for creatures with our perceptual and cognitive capacities. One key principle is that natural concepts should be easily learnable. While evidence exists for parts of this optimal design proposal, there has been no direct evidence linking naturalness to learning until now. This article presents results from a computational study on perceptual color space, demonstrating that naturalness indeed facilitates (...)
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