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.

Support vector machine methods in pattern recognition of sedimentary facies

Abstract: Aiming at the problem of pattern recognition in sedimentary facies analysis, we put forward a scheme which apply SVM to sedimentary facies recognition. Unlike traditional method try to reduce the dimension of input space(i.e. characters selection and transformation), SVM increase dimension of input space to ensure it is Linearly Separable in high dimension space. The method is feasible because it only changes inner product operation and the complexity of algorithm doesn't increase. Using SVM we needn't wasting time on character extraction but resort to the intrinsic character extraction ability which makes it more suitable in practical instance. The result of practical application indicates that the performance of SVM had superiority over RBFNN and overcome the problem of overfitting excellently.

Misfire Detection in Spark-Ignition Engine using Statistical Learning Theory

Abstract: Misfire in an Internal Combustion engine is a serious problem that needs to be addressed to prevent engine power loss, fuel wastage and emissions. The vibration signal contains the vibration signature due to misfire and a combination of all vibration emissions of various engine components. The vibration signals acquired from the engine block are used here. Descriptive statistical features are used to represent the useful information stored in vibration signals. Out of all the statistical features, useful features were identified using the J48 decision tree algorithm and then the selected features were classified using logistic and simple logistic functions. In this paper, performance analysis of logistic and simple logistic function has presented for detecting misfire in Spark Ignition (SI) Engine.

Optimal choice: new machine learning problem and its solution

Abstract: The task of learning to pick a single preferred example out a finite set of examples, an "optimal choice problem", is a supervised machine learning problem with complex, structured input. Problems of optimal choice emerge often in various practical applications. We formalize the problem, show that it does not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.

Assessing Supervised Learning Algorithms

Abstract: Chapter 2 introduced the concepts and formulation developed in the context of the Statistical Learning Theory. In this chapter, those concepts are illustrated using the following algorithms: Distance-Weighted Nearest Neighbors, Perceptron, Multilayer Perceptron, and Support Vector Machines.

Consistency of Empirical Risk Minimization for Unbounded Loss Functions

Abstract: The theoretical framework of Statistical Learning Theory (SLT) for pattern recognition problems is extended to comprehend the situations where an infinite value of the loss function is employed to prevent misclassifications in specific regions with high reliability.