Computational Learning Theory and Natural Learning Systems: Selecting good models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Computational Learning Theory and Natural Learning Systems: Selecting good models PDF full book. Access full book title Computational Learning Theory and Natural Learning Systems: Selecting good models by Stephen José Hanson. Download full books in PDF and EPUB format.
Author: Stephen José Hanson Publisher: Bradford Books ISBN: Category : Computers Languages : en Pages : 448
Book Description
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.
Author: Stephen José Hanson Publisher: Bradford Books ISBN: Category : Computers Languages : en Pages : 448
Book Description
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.
Author: Russell Greiner Publisher: MIT Press ISBN: 9780262571180 Category : Computational learning theory Languages : en Pages : 440
Book Description
This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and Ǹatural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). 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. Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E.M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S.V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.
Author: Stephen José Hanson Publisher: Mit Press ISBN: 9780262581332 Category : Computers Languages : en Pages : 449
Book Description
Annotation These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. George A. Drastal is Senior Research Scientist at Siemens Corporate Research. Ronald J. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology.
Author: Robert E. Schapire Publisher: MIT Press ISBN: 0262526034 Category : Computers Languages : en Pages : 544
Book Description
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
Author: Sanjay Jain Publisher: MIT Press ISBN: 9780262100779 Category : Computers Languages : en Pages : 346
Book Description
This introduction to the concepts and techniques of formal learning theory is based on a number-theoretical approach to learning and uses the tools of recursive function theory to understand how learners come to an accurate view of reality.
Author: Shai Ben David Publisher: Springer Science & Business Media ISBN: 3540233563 Category : Computers Languages : en Pages : 519
Book Description
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.
Author: Christoph Molnar Publisher: Lulu.com ISBN: 0244768528 Category : Computers Languages : en Pages : 320
Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Balaji Krishnapuram Publisher: CRC Press ISBN: 143983928X Category : Computers Languages : en Pages : 316
Book Description
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect
Author: Luc de Raedt Publisher: Springer Science & Business Media ISBN: 3540425349 Category : Computers Languages : en Pages : 527
Book Description
This book constitutes the refereed proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001, held in Freiburg, Germany, in September 2001. The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning.
Author: Stephen José Hanson
Author: Stephen José Hanson
Author: Russell Greiner
Author: Stephen José Hanson
Author: Stephen José Hanson
Author: Robert E. Schapire
Author: Sanjay Jain
Author: Shai Ben David
Author: Christoph Molnar
Author: Balaji Krishnapuram
Author: Luc de Raedt