2009年新书，非扫描 Contents List of Figures xiii List of Tables xix Introduction xxi About the Editors xxvii Contributor List xxix 1 Analysis of Text Patterns Using Kernel Methods 1 Marco Turchi, Alessia Mammone, and Nello Cristianini 1.1 Introduction . .
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques Emilio Soria Olivas University of Valencia, Spain José David Martín Guerrero University of Valencia, Spain Marcelino Martinez Sober University of V
PmSVM (Power Mean SVM), a classifier that trains significantly faster than state-of-the-art linear and non-linear SVM solvers in large scale visual classification tasks, is presented. PmSVM also achieves higher accuracies. A scalable learning method
Chapter 1. Introduction 1.1 Is Pattern Recognition Important? 1.2 Features, Feature Vectors, and Classifiers 1.3 Supervised, Unsupervised, and Semi-Supervised Learning 1.4 MATLAB Programs 1.5 Outline of the Book Chapter 2. Classifiers Based on Bayes
This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpre
One of the most exciting recent developments in machine learning is the discovery and elaboration of kernel methods for classification and regression. These algorithms combine three important ideas into a very successful whole. From mathematical pro
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Algorithms for hyper-parameter optimization.pdf，讲述贝叶斯算法的TPE过程的专业论文The contribution of this work is two novel strategies for approximating f by modeling H: a hier
archical Gaussian Process and a tree-structured parzen estimator. These are described in
Non-intrusive inspection systerms based on X-ray radiography techriques are rou tinely used at transport hubs to ensure the conforrmity of catgo content with the supplied shipping manifest. As trade volurmes increase and regulatiors become more strin
As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). However, the results of pure pixel-wise spectral classifiers often appear very noi
Driven by the need of a plethora of machine learning applications, several attempts have been made at improving the performance of classifiers applied to imbalanced datasets. In this paper, we present a fast maximum entropy machine(MEM)combined with
