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Pattern Recognition and Machine Learning
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Christopher M Bishop
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing ...
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Trevor Hastie, Jerome Friedman, Robert Tibshirani
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as ...
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Reinforcement Learning: An Introduction
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Richard S Sutton, Andrew G Barto
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. This text aims to provide a clear and simple account of the key ideas and algorithms of ...
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Genetic Algorithms in Search, Optimization, and Machine Learning
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David E. Goldberg
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer ...
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Introduction Support Vector Machine
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Nello Cristianini, John Shawe-Taylor
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, ...
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Into the Heart of the Mind: An American Quest for Artificial Intelligence
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Frank Rose
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Artificial Intelligence
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Time-Life Books
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Genetic Algorithms for Pattern Recognition
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Pal K Pal, Sankar K Pal (Editor), Dr. Paul P Wang, M.D (Editor)
Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science ...
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Perceptrons - Expanded Edition: An Introduction to Computational Geometry
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Marvin L Minsky, Seymour A Papert
"Perceptrons" - the first systematic study of parallelism in computation - has remained a classical work on threshold automata networks for nearly two decades. It marked a historical turn in artificial intelligence, and it is required reading for anyone who wants to understand the connectionist counterrevolution that is going on today. Artificial ...
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Introduction to Machine Learning
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Ethem Alpaydin
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed ...
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Machine Learning: An Artificial Intelligence Approach
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Ryszard S. Michalski (Editor), T. W. Mitchel (Editor), Tom M. Mitchell (Editor)
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Learning from Data: Concepts, Theory, and Methods
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Vladimir Cherkassky, Filip M Mulier
An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic ...
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Machine Learning
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Tom M Mitchell
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
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The Artificial Intelligence Debate: False Starts, Real Foundations
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Professor Stephen R Graubard (Editor)
In this broad and provocative collection of original essays on artificial intelligence today, eighteen distinguished scholars set out to explain why and how certain discoveries about the human brain have or have not been used by the AI community, and whether the AI endeavor has increased out, understanding of human cognition. Their exploration ...
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Bayesian Networks and Decision Graphs
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Finn B Jensen
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query ...
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Intelligence: The Eye, the Brain, and the Computer
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Martin A. Fischler, Fischler A. Martin, Oscar Firschein
This book treats the question of how far we have come in understanding intelligence and in duplicating it mechanically. The major facets of intelligence--reasoning, vision, language and learning are discussed as an approach to contrasting biological intelligence with current computer realizations.
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Imitation in Animals and Artifacts
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Chrystopher L Nehaniv (Editor), Kerstin Dautenhahn (Editor)
The effort to explain the imitative abilities of humans and other animals draws on fields as diverse as animal behavior, artificial intelligence, computer science, comparative psychology, neuroscience, primatology, and linguistics. This volume represents a first step toward integrating research from those studying imitation in humans and other ...
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Machine learning of inductive bias
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Utgoff
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Symbolic Visual Learning
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Katsushi Ikeuchi (Editor), Manuela Velosa (Editor)
Some of the fundamental constraints of automated machine vision have been the inability automatically to adapt parameter settings or utilize previous adaptations in changing environments. This work presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome ...
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Computation and Intelligence: Collected Readings
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George F Luger (Editor)
"Computation and Intelligence" brings together readings in artificial intelligence that are particularly relevant to today's student/practitioner. With its critique of the selections, extensive bibliography, and clear presentation of the material, "Computation and Intelligence" should be a useful adjunct to any course in AI as well as a handy ...
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Into the heart of the mind
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Frank- Rose
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Concept Data Analysis: Theory and Applications
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Claudio Carpineto, Giovanni Romano
With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. "Concept Data Analysis: Theory & ...
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Advances in Large-Margin Classifiers
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Alex J Smola (Editor), Peter Bartlett (Editor), Bernhard Scholkopf (Editor)
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks and support vector machines. The fact that it is the margin, or confidence level, of a classification - that is, a scale parameter - rather ...
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Planning and Learning by Analogical Reasoning
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Manuela M Veloso
This research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem ...
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems
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Tim Kovacs
The Distinguished Dissertations series is published on behalf of the Conference of Professors and Heads of Computing and the British Computer Society, who annually select the best British PhD dissertations in computer science for publication. The dissertations are selected on behalf of the CPHC by a panel of eight academics. Each dissertation ...
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