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Learning And Categorization In Modular Neural Networks
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Book Synopsis Learning and Categorization in Modular Neural Networks by : Jacob M.J. Murre
Download or read book Learning and Categorization in Modular Neural Networks written by Jacob M.J. Murre and published by Psychology Press. This book was released on 2014-02-25 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.
Book Synopsis Learning and Categorization in Modular Neural Networks by : Jacob Murre
Download or read book Learning and Categorization in Modular Neural Networks written by Jacob Murre and published by Psychology Press. This book was released on 1992 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.
Book Synopsis Learning and Categorization in Modular Neural Networks by : Jacob M.J. Murre
Download or read book Learning and Categorization in Modular Neural Networks written by Jacob M.J. Murre and published by Psychology Press. This book was released on 2014-02-25 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.
Book Synopsis Modular Learning in Neural Networks by : Tomas Hrycej
Download or read book Modular Learning in Neural Networks written by Tomas Hrycej and published by Wiley-Interscience. This book was released on 1992-10-09 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Modular Learning in Neural Networks covers the full range of conceivable approaches to the modularization of learning, including decomposition of learning into modules using supervised and unsupervised learning types; decomposition of the function to be mapped into linear and nonlinear parts; decomposition of the neural network to minimize harmful interferences between a large number of network parameters during learning; decomposition of the application task into subtasks that are learned separately; decomposition into a knowledge-based part and a learning part. The book attempts to show that modular learning based on these approaches is helpful in improving the learning performance of neural networks. It demonstrates this by applying modular methods to a pair of benchmark cases - a medical classification problem of realistic size, encompassing 7,200 cases of thyroid disorder; and a handwritten digits classification problem, involving several thousand cases. In so doing, the book shows that some of the proposed methods lead to substantial improvements in solution quality and learning speed, as well as enhanced robustness with regard to learning control parameters.".
Book Synopsis New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic by : Jonathan Amezcua
Download or read book New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic written by Jonathan Amezcua and published by Springer. This book was released on 2018-02-05 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.
Author :Vassilios Petridis Publisher :Springer Science & Business Media ISBN 13 :9780792382904 Total Pages :336 pages Book Rating :4.3/5 (829 download)
Book Synopsis Predictive Modular Neural Networks by : Vassilios Petridis
Download or read book Predictive Modular Neural Networks written by Vassilios Petridis and published by Springer Science & Business Media. This book was released on 1998-09-30 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
Book Synopsis Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation by : Daniela Sanchez
Download or read book Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation written by Daniela Sanchez and published by Springer. This book was released on 2016-02-23 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.
Book Synopsis Categorization and learning in neural networks by : Jacob M. Murre
Download or read book Categorization and learning in neural networks written by Jacob M. Murre and published by . This book was released on 1992 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Towards Modular Neural Networks with Pre-trained Models by : Tuan Quang Dinh (Ph.D.)
Download or read book Towards Modular Neural Networks with Pre-trained Models written by Tuan Quang Dinh (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Utilizing pre-trained models for knowledge transfer, or adaptation, has gained widespread adoption in deep learning tasks, owing to its superior efficiency and effectiveness compared to traditional training from scratch. As model sizes continue to expand, freezing pre-trained models has emerged as a viable practice for knowledge transfer, improving data and storage efficiency while mitigating the long-standing issue of catastrophic forgetting. This thesis investigates the potential for solving novel machine learning tasks by assembling frozen pre-trained models into a modular neural network. We employ proficient pre-trained models as building blocks of the modular network, examining various assembly strategies to optimize task performance while preserving inherent efficiency. Our findings demonstrate that this framework can deliver highly effective and efficient solutions across diverse learning contexts. In the sub-task adaptation setting, we propose a method called InRep+, designed to reprogram frozen unconditional generators for conditional generation. This approach achieves high-performance generation while exhibiting robustness against imbalanced and noisy supervision. For cross-modal adaptation, our language-interfaced adaptation procedure enables large pre-trained language models to excel in non-language tasks without any architectural modifications. Moreover, we show that frozen language-image pre-trained models can be effectively and efficiently used for composing visual and topological word similarities, creating a robust unsupervised word translation system. Lastly, we propose modular ensemble methods to augment pre-trained code language models in correcting potentially buggy code, an area where single models fail dramatically. This thesis stands as a pioneering contribution to the comprehension and development of methodologies for constructing modular deep neural network systems utilizing pre-trained models.
Book Synopsis Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition by : Patricia Melin
Download or read book Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition written by Patricia Melin and published by Springer Science & Business Media. This book was released on 2011-10-18 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.
Book Synopsis Towards Modular Neural Networks with Pre-trained Models by : Tuan Quang Dinh (Ph.D.)
Download or read book Towards Modular Neural Networks with Pre-trained Models written by Tuan Quang Dinh (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Utilizing pre-trained models for knowledge transfer, or adaptation, has gained widespread adoption in deep learning tasks, owing to its superior efficiency and effectiveness compared to traditional training from scratch. As model sizes continue to expand, freezing pre-trained models has emerged as a viable practice for knowledge transfer, improving data and storage efficiency while mitigating the long-standing issue of catastrophic forgetting. This thesis investigates the potential for solving novel machine learning tasks by assembling frozen pre-trained models into a modular neural network. We employ proficient pre-trained models as building blocks of the modular network, examining various assembly strategies to optimize task performance while preserving inherent efficiency. Our findings demonstrate that this framework can deliver highly effective and efficient solutions across diverse learning contexts. In the sub-task adaptation setting, we propose a method called InRep+, designed to reprogram frozen unconditional generators for conditional generation. This approach achieves high-performance generation while exhibiting robustness against imbalanced and noisy supervision. For cross-modal adaptation, our language-interfaced adaptation procedure enables large pre-trained language models to excel in non-language tasks without any architectural modifications. Moreover, we show that frozen language-image pre-trained models can be effectively and efficiently used for composing visual and topological word similarities, creating a robust unsupervised word translation system. Lastly, we propose modular ensemble methods to augment pre-trained code language models in correcting potentially buggy code, an area where single models fail dramatically. This thesis stands as a pioneering contribution to the comprehension and development of methodologies for constructing modular deep neural network systems utilizing pre-trained models.
Book Synopsis Architecture and Function of Modular Neural Networks by : Bart Happel
Download or read book Architecture and Function of Modular Neural Networks written by Bart Happel and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Neural Networks, 2 by : I. Aleksander
Download or read book Artificial Neural Networks, 2 written by I. Aleksander and published by Elsevier. This book was released on 2014-06-28 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume proceedings compilation is a selection of research papers presented at the ICANN-92. The scope of the volumes is interdisciplinary, ranging from the minutiae of VLSI hardware, to new discoveries in neurobiology, through to the workings of the human mind. USA and European research is well represented, including not only new thoughts from old masters but also a large number of first-time authors who are ensuring the continued development of the field.
Book Synopsis Using Modular Neural Networks with Local Representations to Control Dynamic Systems by : Christopher G. Atkeson
Download or read book Using Modular Neural Networks with Local Representations to Control Dynamic Systems written by Christopher G. Atkeson and published by . This book was released on 1991 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this research is to develop an artificial neural network with very fast learning. Major areas of activity during this period include developing cross validation methods to refine parameters such as the distance metric, developing parallel versions of the learning algorithms which allow implementations to be scaled up by simply adding additional processing hardware, with a negligible penalty in processing time, and performing numerical experiments on simulated data to test the approach. We have also compared our approach with other neural network approaches, and found that it provides equal or better performance. We have implemented versions of this approach on several platforms: serial computers (standard Sun workstations), digital signal processors (Intel i860), a parallel computer (Connection Machine), and using special purpose circuitry. We are now convinced we can perform training and access sufficiently quickly to allow real time learning.
Book Synopsis Structural adaptation and generalization in modular neural networks by : Viswanath Ramamurti
Download or read book Structural adaptation and generalization in modular neural networks written by Viswanath Ramamurti and published by . This book was released on 1997 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Evolution of Modular Neural Networks by : Victor Manuel Landassuri Moreno
Download or read book Evolution of Modular Neural Networks written by Victor Manuel Landassuri Moreno and published by . This book was released on 2012 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pattern Recognition and Neural Networks by : Ludmila Kuncheva
Download or read book Pattern Recognition and Neural Networks written by Ludmila Kuncheva and published by Lulu.com. This book was released on 2019 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
