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.

Output Prediction Model in Fully Mechanized Mining Face Based on Support

Abstract: Support Vector Machine is a new machine learning technique developed on the basis of Statistical Learning Theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model by programming based on Mat lab, finally, compared with genetic neural network prediction model. It shows that SVM has a higher accuracy of prediction than GNN, which proved the validity and practicality of the model. Keywords-SupportVectorMachine;Fully Mechanized Mining Face; Prediction I. INTRODUCTION The study of the relationship between resources condition and exploitation indicators has become an important part and parcel, which include planning and designing of the mine, development planning of production mine, indicators identified of contracting face and information management and decision-making. A model is established for predicting the output in fully-mechanized mining face for systematization of the output system, which is propitious to adjust and dispose the scale of investment and decision-making direction of the mine production. At present, there are no uniform methods to establish nonlinear system model. At the same time neural networks and genetic algorithms are used more than anyone else, but the effectiveness of these algorithms are based on a relatively large of training samples while support vector machines is a new machine learning technique methods, which developed on the basis of statistical learning theory aimed at a small sample data. It embodies the structural risk minimization (SRM) principle, whose excellent performance causes many researchers to promote attention and get a better application. In this paper the basic principles and characteristics of SVM was expounded at first, and then advanced a method of output prediction model in fully mechanized mining face based on SVM. Finally, the results showed that the method prediction model based on SVM can be achieved a better prediction under a limited sample condition.

Evolutionary Optimization Of Support Vector Machines

Abstract: Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce.

Constrained Learning With Non-Convex Losses

Abstract: Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern statistical problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization, even obtaining a model that satisfies statistical constraints can be challenging. All the more so, a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap -- i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex) statistical problem -- and provide a practical constrained learning algorithm. These results establish a constrained counterpart to classical learning theory, enabling the explicit use of constraints in learning. We illustrate this theory and algorithm in rate-constrained learning applications arising in fairness and adversarial robustness.

Machine Learning Algorithm Based on Relevance Vector Machine

Abstract: Correlation vector machine (RM) is a sparse probability model based on Bayesian theory. It uses the idea of conditional distribution and maximum likelihood estimation to transform the nonlinear problem in low dimensional space into linear problem in high dimensional space through kernel function. It has the advantages of good learning ability, strong generalization ability, flexible kernel function selection and simple parameter setting. Because of its excellent learning performance, it has become a research hotspot in the field of machine learning. This paper introduces the classical relevance vector machine algorithm and its improved model, focuses on the ideas, methods and effects of using relevance vector machine algorithm to solve the classification and prediction problems in fault detection, pattern recognition, Cyberspace Security and other fields, summarizes the existing problems, and prospects the future research direction.

Learning Algorithm of Svm Reduce The Optimization Error And Give The Maximum Accuracy of The QP

Abstract: The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience. SVMs (Support Vector Machines) are a useful technique for data classification. Support Vector Machine (SVM) is a linear machine working in the highly dimensional feature space formed by the nonlinear mapping of the N-dimensional input vector x into a K-dimensional feature space (K>N) through the use of a mapping Φ (x). The data points corresponding to the non-zero weights are called support vectors. The main goal is to measure the error to get the exact solution can be approximated by a function and also get the error accurately to determine the best function implemented by learning system using finite training set and testing set (unseen). The best function closely measure the optimization error in finite training set then the function have less approximation to lead a large estimation error. The main goal of learning algorithm is minimize the training set or time. Smaller constraint by the number of training data, the error is dominated by the approximation then the optimization error can be reduced the iterative time. Indian Jour al of Applied Resear h Website: www.theglobaljournals.com (ISSN 2249-555X) INTRODUCTION Machine learning system is trained by using a sample set of training data. SVMs estimate a linear decision function; mapping of the data into a higherdimensional feature space may be needed. This mapping is characterized by the choice of a class of functions known as kernels [1]. The foundations of Support Vector Machines (SVM) have been developed by Vapnik [2]. A step in SVM classification involves identification as which are intimately connected to the known classes. This is called feature selection or feature extraction. Support Vector Machine (SVM) is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy while automatically avoiding over-fit to the data. Support Vector machines can be defined as systems which use hypothesis space of a linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. Each instance in the training set contains one target values and several variables. SVM CLASSIFICATION The training set is said to be linearly separable when there exists a linear discriminant function whose sign matches the class of all training examples. When a training set is linearly separable there usually is infinity of separating hyperplane. When the data set is large this optimization problem becomes very challenging, because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets (1, 00,000 data points) [3]. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. Optimal Hyperplane The SVM classification technique and show how it leads to the formulation of a QP programming problem in a number of variables that is equal to the number of data points. The data set is linearly separable, and to find the best hyperplane that separates the data [4]. 1 ) ( ( 2 1 ) , min( 2