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.

Application of support vector machines in viral biology

Abstract: Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.

Emergency Classification Based on Support Vector Machine

Abstract: Natural disasters,and issues related to public health and social security have occurred frequently Emergency classification is a method to identify emergency events rapidly and accurately,and divide them into different levels The appropriate emergency measures are taken according to classification results At present,the actual classification decisions still rely mainly on intuition and experience of decision-makers In order to obtain the results of scientific classification,it needs to establish a classification model based on objective data and provides a scientific basis for decision-makers Support vector machine( SVM) is developed based on statistical learning theory It is an effective way to deal with classification problems This paper presents the application of support vector machine in order to solve emergency classification problemsSupport vector machine algorithm is originally designed for binary classification problem,and then used for multi-category classification problem There are two methods for extending SVM to multi-class SVM The first method is to construct a multi-class classifier by combing several binary classifiers The second method is to consider all data in an optimized formula for global optimization There are four ways to solve multi-class recognition problems through a combination of multiple binary classifiers: oneagainst-all,one-against-one,directed acidic graph and binary tree In this paper,one-against-one SVM is selected to solve emergency classification problemsThis paper describes the emergency classification process based on support vector machine The process consists of five steps:( 1) establish an index system based on emergency types and the analysis of relevant factors;( 2) collect historical data of the index system to constitute the SVM training sample set;( 3) use a training set to classify learning according to the SVM classification algorithm and obtain an inherent law;( 4) find support vector of the training set to construct SVM decision function; and( 5) enter the index data of classification object into the decision function and obtain the classification resultsThe classification of earthquake disasters is an example to verify the feasibility of the emergency classification method based on support vector machine According to an earthquake disaster database from the National Earthquake Science Data Sharing Center website,30 earthquake sample data from 1995 to 2004 were selected,with 24 of them as training data,and others as the testing dataSelected seismic data contain nine characteristic properties: Richter scale,epicenter intensity,VI degree area,the number of people affected,the number of deaths,the number of injuries,the number of houses destroyed,the number of total housing damage and direct economic losses According to the calculation result of LibSVM toolkit,experimental results show that support vector machine is effective to solve classification problems related to earthquake disastersIn the present paper,an emergency classification method based on support vector is introduced Earthquake classification is proposed as an example The one-against-one method of multi-class support vector machine is applied to the experiment Experimental results show that multi-class support vector machine is a feasible approach dealing with emergency classification problems

A Learning Theory Framework for Association Rules and Sequential Events

Abstract: We present a framework and generalization analysis for the use of association rules in the setting of supervised learning. We are specifically interested in a sequential event prediction problem where data are revealed one by one, and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, to our knowledge there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules. These algorithms can be used both for sequential event prediction and for supervised classification. We provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an "adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis.

Using Kernel Methods in a Learning Machine Approach for Multispectral Data Classification. An Application in Agriculture

Abstract: Most pattern recognition applications within the Geoscience field involve the clustering and classification of remote sensed multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the complexity of this problem is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface heterogeneity, remote sensing effects and multispectral features. The present chapter describes recent developments in the performance of a kernel method applied to the representation and classification of agricultural land use systems described by multispectral responses. In particular, we focus on the practical applicability of learning machine methods to the task of inducting a relationship between the spectral response of farms land cover to their informational typology from a representative set of instances. Such methodologies are not traditionally used in agricultural studies. Nevertheless, the list of references reviewed here show that its applications have emerged very fast and are leading to simple and theoretically robust classification models. This chapter will cover the following phases: a)learning from instances in agriculture; b)feature extraction of both multispectral and attributive data and; c) kernel supervised classification. The first provides the conceptual foundations and a historical perspective of the field. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions.

Network intrusion detection method based on parallel support vector machines

Abstract: In this paper,a network intrusion detection method based on PSVMs is constructed.Multiple PSVMs can run at distributed computer system environment.Using feedback to update the initial classifiers,avoid the problem that the learning performance is subject to the distribution state of the data samples in different subsets.Comparison of detection ability between the above detection method and BP neural network,the experiments show that this method can achieve high detection efficiency and low false positive efficiency.And it is superior to traditional BP network method in training time,and has better general ability.