A Code for Classifiers
Author: William Stetson Merrill
Publisher:
Published: 1914
Total Pages: 274
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: William Stetson Merrill
Publisher:
Published: 1914
Total Pages: 274
ISBN-13:
DOWNLOAD EBOOKAuthor: Corinne Bacon
Publisher:
Published: 1916
Total Pages: 42
ISBN-13:
DOWNLOAD EBOOKAuthor: Steven Bird
Publisher: "O'Reilly Media, Inc."
Published: 2009-06-12
Total Pages: 506
ISBN-13: 0596555717
DOWNLOAD EBOOKThis book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
Author: Ralf Herbrich
Publisher: MIT Press
Published: 2001-12-07
Total Pages: 402
ISBN-13: 9780262263047
DOWNLOAD EBOOKAn overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Author: Lior Rokach
Publisher: World Scientific
Published: 2010
Total Pages: 242
ISBN-13: 9814271071
DOWNLOAD EBOOK1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning - post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I - training an ensemble given a code-matrix. 6.3. Type II - adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used?
Author: Margaret Mann
Publisher:
Published: 1928
Total Pages: 478
ISBN-13:
DOWNLOAD EBOOKAuthor: Friedhelm Schwenker
Publisher: Springer
Published: 2015-06-02
Total Pages: 240
ISBN-13: 3319202480
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 12th International Workshop on Multiple Classifier Systems, MCS 2015, held in Günzburg, Germany, in June/July 2015. The 19 revised papers presented were carefully reviewed and selected from 25 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics. They are organized in topical sections on theory and algorithms and application and evaluation.
Author: Michael Codish
Publisher: Springer
Published: 2014-05-22
Total Pages: 367
ISBN-13: 3319071513
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 12th International Symposium on Functional and Logic Programming, FLOPS 2014, held in Kanazawa, Japan, in June 2014. The 21 full papers and 3 invited talks presented in this volume were carefully reviewed and selected from 41 submissions. They deal with declarative programming, including functional programming and logic programming.
Author: Australian Law Reform Commission
Publisher: ALRC
Published: 2012
Total Pages: 404
ISBN-13: 0987177737
DOWNLOAD EBOOKAuthor: United States. Congress. House. Committee on Government Operations
Publisher:
Published: 1973
Total Pages: 128
ISBN-13:
DOWNLOAD EBOOK