Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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正則化 (Regularization) の最近の動向

Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem. The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime. This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.

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Feature selection of gene expression data for Cancer classification using double RBF-kernels.

It has been recently pointed out that the Regularized Least Squares Classifier (RLSC), continues to be a viable option for binary classification problems. We apply RLSC to the datasets of the NIPS 2003 Feature Selection Challenge using Gaussian kernels. Since RLSC is sensitive to noise variables, ensemble-based variable filtering is applied first. RLSC makes use of the best-ranked variables only. We compare the performance of a stochastic ensemble of RLSCs to a single best RLSC. Our results indicate that in terms of classification error rate the two are similar on the challenge data. However, especially with large data sets, ensembles could provide other advantages that we list.

Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems

Support vector machines can be regarded as algorithms for compressing information about class membership into a few support vectors with clear geometric interpretation. It is tempting to use this compressed information to select the most relevant input features. In this paper we present a method for doing so and provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods.

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Efficient Feature Subset Selection for Support Vector Machines

This thesis studies the problem of supervised learning using a family of machines, namely kernel learning machines. A number of standard learning methods belong to this family, such as Regularization Networks (RN) and Support Vector Machines (SVM). The thesis presents a theoretical justification of these machines within a unified framework based on the statistical learning theory of Vapnik. The generalization performance of RN and SVM is studied within this framework, and bounds on the generalization error of these machines are proved. In the second part, the thesis goes beyond standard one-layer learning machines, and probes into the problem of learning using hierarchical learning schemes. In particular it investigates the question: what happens when instead of training one machine using the available examples we train many of them, each in a different way, and then combine the machines? Two types of ensembles are defined: voting combinations and adaptive combinations. The statistical properties of these hierarchical learning schemes are investigated both theoretically and experimentally: bounds on their generalization performance are proved, and experiments characterizing their behavior are shown. Finally, the last part of the thesis discusses the problem of choosing data representations for learning. It is an experimental part that uses the particular problem of object detection in images as a framework to discuss a number of issues that arise when kernel machines are used in practice. Thesis Supervisor: Tomaso Poggio Title: Uncas and Helen Whitaker Professor of Brain and Cognitive Sciences

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Theodoros K. Evgeniou

Papers

Learning with kernel machine architectures