Machine Learning Books - Page 5

Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)

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Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Vision

Editorial Review:

Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.

The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

Pierre Baldi, Søren Brunak

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Customer Reviews:
Total reviews: 16 Average rating: 3.5 of 5

Editorial Review:

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models—and to automate the process as much as possible.

In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.

This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

Prediction, Learning, and Games

Nicolo Cesa-Bianchi, Gabor Lugosi

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Subjects -> Computers & Internet -> Graphic Design -> General
Subjects -> Computers & Internet -> Graphic Design -> General AAS

Editorial Review:

This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections. Old and new forecasting methods are described in a mathematically precise way in order to characterize their theoretical limitations and possibilities.

Planning Algorithms

Steven M. LaValle

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Subjects -> Computers & Internet -> Programming -> Algorithms -> General
Subjects -> Computers & Internet -> Programming -> Algorithms -> General AAS

Editorial Review:

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.

Learning from Data: Concepts, Theory, and Methods

Vladimir Cherkassky, Filip M. Mulier

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Customer Reviews:
Total reviews: 3 Average rating: 4.5 of 5

Study in easy 5 out of 5 stars.
11 of 14 people found this review helpful.

This book is excellent and easy to study. Graduate students will find the book statistical learning theory and support vector machines(SVMs),especially learning system based on recent advances in machine learning and multiobjective optimization. This book describes the Vapnik and Chervonenkis(VC) theory's generalization abilities. For statisticians, Applied mathematician, mechanical engineers and most graduate student are interested in reading this book. This is a very good excellent reference!!

Editorial Review:

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 can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Large-Scale Kernel Machines (Neural Information Processing)

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Editorial Review:

Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms.

After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.

Contributors:
Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, and Elad Yom-Tov

Machine Learning and Data Mining: Introduction to Principles and Algorithms

Igor Kononenko, Matjaz Kukar

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Information and Randomness: An Algorithmic Perspective (Texts in Theoretical Computer Science. An EATCS Series)

Cristian S. Calude

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Customer Reviews:
Total reviews: 1 Average rating: 5.0 of 5

Classical ideas with modern use. 5 out of 5 stars.
10 of 13 people found this review helpful.

I stumbled over this (lovely) book a little by accident. As I kept reading, my enthusiasm for the book gradually increased. While the book is addressed perhaps more to students in computation and in CS, it is very attractive also as a text to be used in mainstream mathematics, and in probability theory.

It begins with a new look at the classical Kolmogorov construction of measures on infinite product spaces, and asks for explicit ways of labeling them with a class of certain concrete numerical functions. Then it moves onto noiseless coding theory (from communications science), but it stays rooted firmly in classical ideas from Shannon-Kolmogorov communication and information theory.

It is indeed pleasing to see that God still plays dice, not only in quantum theory, but also in such classical areas of math as in number theory.
From the foreword: "...putting Shannon's information theory and Turing's computability theory into a cocktail shaker, and shaking vigorously..."

The book is a second edition 2002, with a number of attractive additions to the first edition from 1994. It will likely work equally well in a course, as for self-study.
The main portion in the book focuses on classical and modern topics in computability, and its connections to randomness; covering concrete halting problems, chaos, cellular automata, algorithms, and their complexity.
Palle Jorgensen, October 2004.

Editorial Review:

Presents in a mathematically clear way the fundamentals of algorithmic information theory and a few selected applications to mathematical logic.

Advances in Large-Margin Classifiers (Neural Information Processing)

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Editorial Review:

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 than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

Practical Genetic Algorithms

Randy L. Haupt, Sue Ellen Haupt

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Customer Reviews:
Total reviews: 11 Average rating: 4.0 of 5

Not a good place to start 1 out of 5 stars.
13 of 15 people found this review helpful.

Presents non-standard techniques without pointing out the standard ones. The non-standard techniques were recommended strongly based only on author's personal opinions, without comparison to other standard techniques on broad spectrum.

For starters, it is much better to look into "An Introduction to Genetic Algorithms" by Melanie Michell.

Great 5 out of 5 stars.
7 of 7 people found this review helpful.

In my opinion to well understand a process/method you have to follow an example in every little detail. This book does exactly this and once read allows to write your own code easily. I highly recommend this book!

Good, though not good value for money 3 out of 5 stars.
7 of 9 people found this review helpful.

This book is well written, with good examples and insights. However, I think that there should be many more examples and theory to warrent the price of this book. Therefore, better take this book from a library or wait for a softcover.

Editorial Review:

* This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science
* Most significant update to the second edition is the MATLAB codes that accompany the text
* Provides a thorough discussion of hybrid genetic algorithms
* Features more examples than first edition