LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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LIBSVM: A library for support vector machines

There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

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A Tutorial on Support Vector Machines for Pattern Recognition

We present a general method using kernel canonical correlation analysis (KCCA) to learn a semantic of text from an aligned multilingual collection of text documents. The semantic space provides a language-independent representation of text and enables a comparison between the text documents from different languages. In experiments, we apply the KCCA to the cross-lingual retrieval of text documents, where the text query is written in only one language, and to cross-lingual text categorization, where we trained a cross-lingual classifier. IDEA GROUP PUBLISHING This paper appears in the publication, Kernel Methods in Bioengineering, Signal and Image Processing edited by G. Camps-Valls; J. Rojo-Álvarez; M. Martínez-Ramón © 2007, IGI Global 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-pub.com ITB13876 Fortuna, Cristianini, & Shawe-Taylor Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Introduction The ideas of using statistical techniques from communication theory to translate text from one natural language to another have been around for many years. Literally, computational text analysis is as old as computers (which were introduced as part of UK decryption efforts in World War II). A pioneer of information theory, Warren Weaver suggested automatic approaches to crosslanguage analysis as early as 1949, but efforts in this direction were soon abandoned for various technical and theoretical reasons (Weaver, 1955). The idea of automatically detecting relations between languages has not been abandoned, however. Today, state-of-the art methods in automatic translation rely on statistical modeling. In the mid-’30s a statistical technique had been introduced to find correlations between two sets of vectors: canonical correlation analysis (CCA). Various computational and conceptual limitations made a statistical analysis of bilingual text based on CCA impractical (Hoteling, 1936). For one, the vector space model of text documents became popular in information retrieval (IR) only in the 1970s. The recent combination of CCA with kernel methods (Bach & Jordan, 2002; Lai & Fyfe, 2000; Shawe-Taylor & Cristianini, 2004) paved the way to a number of statistical, computational, and conceptual extensions. Various kernels were used to represent semantic relations, dual representation to speed up computations, and regularization-theory ideas to avoid discovering spurious correlations. We present in this chapter a method based on kernel CCA to extract correlations between documents written in different languages with the same content. Given a paired bilingual corpus (a set of pairs of documents, each pair being formed by two versions of the same text in two different languages), this method defines two embedding spaces for the documents of the corpus, one for each language, and an obvious one-to-one correspondence between points in the two spaces. KCCA then finds projections in the two embedding spaces for which the resulting projected values are highly correlated. In other words, it looks for particular combinations of words that appear to have the same co-occurrence patterns in the two languages. Our hypothesis is that finding such correlations across a paired cross-lingual corpus will locate the underlying semantics since we assume that the two languages are conditionally independent, or that the only thing they have in common is their meaning. The directions would carry information about the concepts that stood behind the process of the generation of the text and, although expressed differently in different languages, are, nevertheless, semantically equivalent. A preliminary version of this study appeared in Vinokourov, Shawe-Taylor, and Cristianini (2002). Other methods exist to analyze statistically relations within bilingual text, such as cross-lingual latent semantic indexing (LSI; Littman, Dumais, & Landauer, 1998). LSI (Deerwester, Dumais, Furnas, Landauer, & Harshman, 1990) has been used to extract information about the co-occurrence of terms in the same documents, an indicator of semantic relations, and this is achieved by singular value decomposition (SVD) of the term-document matrix. The LSI method has been adapted to deal with the important problem of cross-language retrieval, where a query in a language is used to retrieve documents in a different language. Using a paired corpus, after merging each pair into a single document, we can interpret frequent 18 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the

A Kernel Canonical Correlation Analysis for Learning the Semantics of Text

Shortcuts to Artificial Intelligence

During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each data set is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion—recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in an optimal way. These methods formulate the problem of optimal kernel combination as a convex optimization problem that can be solved with semi-definite programming techniques. In this paper, we demonstrate the utility of these techniques by investigating the problem of predicting membrane proteins from heterogeneous data, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions. A statistical learning algorithm trained from all of these data performs significantly better than the same algorithm trained on any single type of data and better than existing algorithms for membrane protein classification.

A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction

The strong trend towards the automation of many aspects of scientific enquiry and scholarship has started to affect also the social sciences and even the humanities Several recent articles have demonstrated the application of pattern analysis techniques to the discovery of non-trivial relations in various datasets that have relevance for social and human sciences, and some have even heralded the advent of "Computational Social Sciences" and "Culturomics" In this review article I survey the results obtained over the past 5 years at the Intelligent Systems Laboratory in Bristol, in the area of automating the analysis of news media content This endeavor, which we approach by combining pattern recognition, data mining and language technologies, is traditionally a part of the social sciences, and is normally performed by human researchers on small sets of data The analysis of news content is of crucial importance due to the central role that the global news system plays in shaping public opinion, markets and culture It is today possible to access freely online a large part of global news, and to devise automated methods for large scale constant monitoring of patterns in content The results presented in this survey show how the automatic analysis of millions of documents in dozens of different languages can detect non-trivial macro-patterns that could not be observed at a smaller scale, and how the social sciences can benefit from closer interaction with the pattern analysis, artificial intelligence and text mining research communities

Automatic discovery of patterns in media content

Social machines are systems formed by material and human elements interacting in a structured way. The use of digital platforms as mediators allows large numbers of humans to participate in such machines, which have interconnected AI and human components operating as a single system capable of highly sophisticated behaviour. Under certain conditions, such systems can be understood as autonomous goal-driven agents. Many popular online platforms can be regarded as instances of this class of agent. We argue that autonomous social machines provide a new paradigm for the design of intelligent systems, marking a new phase in AI. After describing the characteristics of goal-driven social machines, we discuss the consequences of their adoption, for the practice of artificial intelligence as well as for its regulation.

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Nello Cristianini

Papers

A Kernel Canonical Correlation Analysis for Learning the Semantics of Text

Shortcuts to Artificial Intelligence

A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction

Automatic discovery of patterns in media content

The social turn of artificial intelligence