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An Introduction to Support Vector Machines
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TLDR
This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.Abstract:
This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.read more
Citations
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Journal ArticleDOI
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
TL;DR: In this paper, the authors evaluated the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discrimination Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances.
Journal ArticleDOI
Corpus-based Learning of Analogies and Semantic Relations
TL;DR: The authors used the Vector Space Model (VSM) of information retrieval to solve SAT analogy questions and achieved state-of-the-art performance for both verbal analogies and noun-modifier relations.
Patent
System and methods for adaptive model generation for detecting intrusion in computer systems
TL;DR: In this paper, a system and methods for detecting intrusions in the operation of a computer system comprises a sensor configured to gather information regarding the operation, to format the information in a data record having a predetermined format, and to transmit the data in the predetermined data format.
Proceedings ArticleDOI
Style mining of electronic messages for multiple authorship discrimination: first results
TL;DR: The results show that stylistic models can be accurately learned to determine an author's identity, based only on the message text.
Journal ArticleDOI
Nanog-dependent feedback loops regulate murine embryonic stem cell heterogeneity
Ben D. MacArthur,Ana Sevilla,Michael Lenz,Franz-Josef Müller,Berhard M Schuldt,Andreas Schuppert,Sonya J. Ridden,Patrick S. Stumpf,Miguel Fidalgo,Avi Ma'ayan,Jianlong Wang,Ihor R. Lemischka +11 more
TL;DR: It is found that early molecular changes subsequent to Nanog loss are stochastic and reversible, and exogenous regulation of Nanog-dependent feedback control mechanisms produced a more homogeneous ES cell population.