Computational Learning Theory And Natural Learning Systems Selecting Good Models
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Computational Learning Theory And Natural Learning Systems
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Author : Stephen José Hanson
language : en
Publisher:
Release Date : 1995
Computational Learning Theory And Natural Learning Systems written by Stephen José Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computational learning theory categories.
Computational Learning Theory And Natural Learning Systems
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Author : Stephen José Hanson
language : en
Publisher:
Release Date : 1995
Computational Learning Theory And Natural Learning Systems written by Stephen José Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computational learning theory categories.
Computational Learning Theory And Natural Learning Systems Selecting Good Models
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Author : Stephen José Hanson
language : en
Publisher: Bradford Books
Release Date : 1994
Computational Learning Theory And Natural Learning Systems Selecting Good Models written by Stephen José Hanson and has been published by Bradford Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.
Computational Learning Theory And Natural Learning Systems Vol Iii
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Author : Thomas Petsche
language : en
Publisher:
Release Date : 1995
Computational Learning Theory And Natural Learning Systems Vol Iii written by Thomas Petsche and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Computational Learning Theory And Natural Learning Systems
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Author : Stephen José Hanson
language : en
Publisher:
Release Date : 1994
Computational Learning Theory And Natural Learning Systems written by Stephen José Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Algorithmic Learning Theory
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Author : Shai Ben David
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-09-23
Algorithmic Learning Theory written by Shai Ben David and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-23 with Computers categories.
Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
Advances In Intelligent Data Analysis
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Author : Frank Hoffmann
language : en
Publisher: Springer
Release Date : 2003-06-30
Advances In Intelligent Data Analysis written by Frank Hoffmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-30 with Computers categories.
This book constitutes the refereed proceedings of the 4th International Conference on Intelligent Data Analysis, IDA 2001, held in Cascais, Portugal, in September 2001.The 37 revised full papers presented were carefully reviewed and selected from a total of almost 150 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, artificial intelligence, neural networks, machine learning, data mining, and interactive dynamic data visualization.
Advances In Neural Information Processing Systems 8
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Author : David S. Touretzky
language : en
Publisher: MIT Press
Release Date : 1996
Advances In Neural Information Processing Systems 8 written by David S. Touretzky and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book
Data Mining And Knowledge Discovery Handbook
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Author : Oded Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-28
Data Mining And Knowledge Discovery Handbook written by Oded Maimon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-28 with Computers categories.
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.