THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.

/pdf/statistical-comparisons-of-classifiers-over-multiple-data-18bm1e3eph.pdf

Statistical Comparisons of Classifiers over Multiple Data Sets

Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.

/pdf/a-comparison-of-methods-for-multiclass-support-vector-4dby8jdi15.pdf

A comparison of methods for multiclass support vector machines

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

This 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.

https://www.foo.be/cours/dess-20122013/b/Natural%20Language%20Processing%20with%20Python%20-%20O'Reilly2009.pdf

Natural Language Processing with Python

Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kernel over the whole input space. Our main objective is the formulation of the localized multiple kernel learning (LMKL) framework that allows kernels to be combined with different weights in different regions of the input space by using a gating model. In this paper, we apply the LMKL framework to regression estimation and derive a learning algorithm for this extension. Canonical support vector regression may over fit unless the kernel parameters are selected appropriately; we see that even if provide more kernels than necessary, LMKL uses only as many as needed and does not overfit due to its inherent regularization.

/pdf/localized-multiple-kernel-regression-2nokche416.pdf

Localized Multiple Kernel Regression

https://www.cmpe.boun.edu.tr/~ethem/files/papers/mil-pr15.pdf

Single- vs. multiple-instance classification

https://www.cmpe.boun.edu.tr/~ethem/files/papers/berk_patrec.pdf

Learning the best subset of local features for face recognition

ANNSyS: an analog neural network synthesis system

One of the major difficulties in face recognition systems is the in-depth pose variation problem. Most face recognition approaches assume that the pose of the face is known. In this work, we have designed a feature based pose estimation and face recognition system using 2D Gabor wavelets as local feature information. The difference of our system from the existing ones lies in its simplicity and its intelligent sampling of local features. Intelligent feature selection can be carried out by learning a set of parameters where the aim is the optimal performance of the overall system. In this paper we give comparative analysis of the performance of our system with the standard modular eigenfaces approach and show that local feature based approach improved the performance of both pose estimation and face recognition. For efficient coding, we have employed principal component analysis to the outputs of local feature vectors. Intelligent feature selection also reduced the space and time complexity of the system while retaining almost the same estimation and recognition accuracy.

Ethem Alpaydin

Papers

Localized Multiple Kernel Regression

Single- vs. multiple-instance classification

Learning the best subset of local features for face recognition

ANNSyS: an analog neural network synthesis system

Feature selection for pose invariant face recognition