Statistical learning theory

About: Statistical learning theory is a research topic. Over the lifetime, 1618 publications have been published within this topic receiving 158033 citations.

A Novel Word Sense Disambiguation Algorithm Based on Semi-Supervised Statistical Learning

Abstract: Statistical learning theory is a framework drawing from the fields of statistics and functional analysis . It provides a strong theoretical foundation for machine learning problems in the system of finite sample case. Word sense disambiguation (WSD) is a fundamental task in natural language processing to identify which sense of a word is used in a sentence, when the word has multiple meanings. At present, the mainstream studies of word sense disambiguation focus on the use of a variety of statistical machine learning techniques. But it difficult to obtain high quality labeled data. To solve the problem, we proposed a novel word sense disambiguation algorithm based on semi-supervised statistical learning in this paper. Firstly, an initial classifier with a certain accuracy rate was constructed based on small-scale labeled data. Then we extend the train data using a variety of threshold. The experiment results show the proposed method has a higher performance for word sense disambiguation.

Teaching \Prediction: Machine Learning and Statistics"

Abstract: The course Prediction: Machine Learning and Statistics is taught currently at MIT to mathematically oriented non-experts. The course focuses generally on predictive modeling from data, and contains topics within data mining, machine learning, and statistics, often going back and forth between machine learning and statistical views of various algorithms and concepts. The course is structured in three parts: an overview of most of the \top 10" algorithms in data mining based on the ICDM survey (Wu et al., 2008), statistical learning theory and kernels, and Bayesian analysis. We present insights from this course.

Research Status of Fault Diagnosis Based on Support Vector Machine

Abstract: Support vector machine has good learning ability and it is good to perform the structural risk minimization principle of statistical learning theory and its application in fault diagnosis of the biggest advantages is that it is suitable for small sample decision. Its nature of learning method is under the condition of limited information to maximize the implicit knowledge of classification in data mining and it is of great practical significance for fault diagnosis. This paper analyzed and summarized the present situation of application of support vector machine in fault diagnosis and made a meaningful exploration on development direction of the future.

Generalization behaviour of alkemic decision trees

Abstract: This paper is concerned with generalization issues for a decision tree learner for structured data called Alkemy. Motivated by error bounds established in statistical learning theory, we study the VC dimensions of some predicate classes defined on sets and multisets – two data-modelling constructs used intensively in the knowledge representation formalism of Alkemy – and from that obtain insights into the (worst-case) generalization behaviour of the learner. The VC dimension results and the techniques used to derive them may be of wider independent interest.

Fault Pattern Recognition of Rolling Bearing Based on K-L Transformation and Support Vector Machine

Abstract: On the basis of statistical learning theory and the feature analysis of vibration signal of rolling bearing,a new method of fault diagnosis based on K-L transformation and support vector machine is presented.Multidimensional correlated variable is transformed into low dimensional independent eigenvector by the means of K-L transformation.The pattern recognition and nonlinear regression are achieved by the method of support vector machine.In the light of the feature of vibration signals,eigenvector is obtained using K-L transformation,fault diagnosis of rolling bearing is recognized correspondingly using support vector machine multiple fault classifier.Theory and experiment shows that the recognition of fault diagnosis of rolling bearing based on K-L transformation and support vector machine theory is available to recognize the fault pattern accurately and provides a new approach to intelligent fault diagnosis.