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.

Study on the Support Vector Machines Model for Sales Volume Prediction and Parameters Selection

Abstract: Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. Support vector machine is used to model enterprise sale amount prediction, and the theory of SVR are briefly described. A simulation example is taken to demonstrate correctness and effectiveness of the proposed approach. The selection method of the model parameters is presented.

Analysis of the automated speaker recognition system of critical use operation results

Abstract: Context. The article summarizes the statistical learning theory to evaluate the long-term operation results of the automated speaker recognition system of critical use (ASRSCU) taking into account the features of the system’s operation object and the structural specificity of such a class of recognition systems. Objective. The goal of the represented work is the development of a complex set of methods for the ASRSCU’s quality parameters stabilization during its long-term operation. Method. The article formulated set of methods for the ASRSCU’s operational risks estimation of its long-term operation. In particular, the dependence of the risk of an incorrect speaker recognition on the features space dimension is described. Based on the formulated measure of informativity, obtained a set of methods to analyze the training sample to identify examples that lead to increased risk. The influence of the phenomenon of the drift of the speech signal parameters on the quality indicators of the ASRSCU is described analytically. An estimation of the operation duration of the ASRSCU, during which it is impractical to re-train its the classifier, is carried out. Recommendations for choosing an optimal ASRSCU’s classifier are formulated from the position of its complexity minimization, taking into account the risks of the ASRSCU’s long-term operation and the possibility of re-training. Results. Represented in the article theoretical results are verified by the DET-curves experiments data, which summarize the information from long-term experiments with the ASRSCU, in which, during the features space configuration were taken into account the features based on the power normalized cepstral coefficients based and the features based on the spectral-temporal receptive fields theory. Within the framework of the created theoretical concept, an estimation of the influence of the features space configuration and the type and complexity of the classifier on the stability of the ASRSCU’s quality parameters during its long-term operation has been carried out. Conclusions. For the first time the theoretically analyzed the problem of average risk minimization by empirical operation results of a ASRSCU, where, unlike existing approaches, non-stationary input data with the drift of individual speech signals features and the characteristic parameters of the recognition system classifier were taken into account, which allowed to estimate the risk’s confidence interval for conditions for re-training sessions.

An Effective Approximation Modeling Method for Ship Resistance in Multidisciplinary Ship Design Optimization

Abstract: Ship design is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional ship design process only involves independent design optimization with some regression formulas within each discipline. With such an approach, there is no guarantee to achieve the optimum design. At the same time, it is also crucial for modem ship design to improve the efficiency of ship optimization. Nowadays, Computational fluid dynamics (CFD) has brought into ship design optimization. However, there are still some problems such as modeling, calculation precision and time consumption even when CFD software is inlaid into the optimization procedure. Modeling is a far-ranging and all-around subject, and its precision directly affects the scientific decision in future. How to use an algorithm to establish a statistical approximation model instead of the CFD calculation will be the key problem. The Support Vector Machines (SVM), a new general machine learning method based on the frame of statistical learning theory, may solve the problems of non-linear classification and regression in sample space and be an effective method of processing the non-liner classification and regression. Recently, Support Vector Regression (SVR) has been introduced to solve regression and modeling problems and been used in wide fields. The classical SVR has two parameters to control the errors. A new algorithm of Support Vector Regression proposed in this article has only one parameter to control the errors, adds b2/2 to the item of confidence interval at the same time, and adopts the Laplace loss function. It is named Single-parameter Lagrangian Support Vector Regression (SPLSVR). This effective algorithm can improve the operation speed of program to a certain extent, and has better fitting precision. In practical design of ship, Design of Experiment (DOE) and the proposed support vector regression algorithm are applied to ship design optimization to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. The result indicates that SPLSVR method to establish approximate models can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

Incremental support vector machine based on center distance ratio

Abstract: Although a Support Vector Machine(SVM) is applicable to a learning task with small training examples,all the training examples don't play an important role in the learning task,but a few ones called support vectors do.According to the relations of support vector,center distance ratio,margin vector and incremental learning,a new method called incremental support vector machine based on center distance ratio was presented.First of all,some support vectors were extracted by the method;then others were made up by the incremental learning method so all the support vectors were found.Compared to the CDRM+SVM,incremental support vector machine based on center distance ratio utilizes effectively center distance ratio and suits to incremental learning.So the new method improves the speed of SVM greatly,while the ability of SVM to classify is unaffected.

Non-linear system identification of an autocatalytic reactor using least squares support vector machines

Abstract: The concepts behind support vector machines are very interesting both in theory and in practice, as they are based on a universal constructive learning procedure derived from the statistical learning theory developed by Vapnik. In this paper, their application to time series modelling is considered by means of simulated data from an autocatalytic reactor. In particular, reconstruction methods from non-linear dynamics are used to define a state space model for the process. Multivariate embedding techniques are compared to scalar embedding with respect to modelling. Keywords: System identification, Support vector machines, Multilayer perceptrons, Non-linear dynamics