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 Genetic Arithmetic and Support Vector Machine in Prediction of Compression Index of Clay

Abstract: The compression index is an important soil property that is essential to many geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming. Support Vector Machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. Considering the fact that parameters in SVM model are difficult to be decided, a genetic SVM was presented in which the parameters in SVM method are optimized by Genetic Algorithm (GA). Taking plasticity index, water content, void ration and density of soil as primary influence factors, the prediction model of compression index based on GA-SVM approach was obtained. The results of this study showed that the GA-SVM approach has the potential to be a practical tool for predicting compression index of soil.

Do Simpler Models Exist and How Can We Find Them

Abstract: While the trend in machine learning has tended towards more complex hypothesis spaces, it is not clear that this extra complexity is always necessary or helpful for many domains. In particular, models and their predictions are often made easier to understand by adding interpretability constraints. These constraints shrink the hypothesis space; that is, they make the model simpler. Statistical learning theory suggests that generalization may be improved as a result as well. However, adding extra constraints can make optimization (exponentially) harder. For instance it is much easier in practice to create an accurate neural network than an accurate and sparse decision tree. We address the following question: Can we show that a simple-but-accurate machine learning model might exist for our problem, before actually finding it? If the answer is promising, it would then be worthwhile to solve the harder constrained optimization problem to find such a model. In this talk, I present an easy calculation to check for the possibility of a simpler model. This calculation indicates that simpler-but-accurate models do exist in practice more often than you might think. I then briefly overview several new methods for interpretable machine learning. These methods are for (i) sparse optimal decision trees, (ii) sparse linear integer models (also called medical scoring systems), and (iii) interpretable case-based reasoning in deep neural networks for computer vision.

A novel fuzzy SVM based on fuzzy c-means for credit scoring

Abstract: Credit scoring has become a very important issue in the financial industry. In order to identify good or bad credit applicants, many literatures with their proposed classification methods had been published to deal with this problem. The support vector machine (SVM) of statistical learning theory was successfully applied in classification. However it still suffers from noise sensitivity originating from the fact that all the data points are treated equally. To tackle this problem, the SVM was extended into a fuzzy SVM (FSVM) by the introduction of fuzzy memberships. In this paper, a new membership calculation method is proposed, which uses a fuzzy c-means (FCM) algorithm to deal with the classification problems with outliers or noises. In the FCM-FSVM algorithm, the training dataset is divided into many clusters by using FCM, then the membership of each of training points belongs to its class can be used to be the membership of FSVM. The proposed algorithm was applied to a credit scoring classification problem, and the results verified the effectiveness of the method.

Statistical-learning control of multiple-delay systems with application to ATM networks

Abstract: Congestion control in the ABR class of ATM network presents interesting challenges due to the presence of multiple uncertain delays. Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to challenging control problems. In this paper, using some recent results by the authors, an efficient statistical algorithm is used to design a robust, fixed-structure, controller for a high-speed communication network with multiple uncertain propagation delays.