Topic
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.
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02 Jun 1999
TL;DR: In this article, a novel approach for studying the performance of estimators in terms of their expected performance is introduced, using ideas from statistical learning theory, and sufficient conditions on the manufacturing process, the estimation algorithm, and the design procedure to guarantee asymptotic convergence of the estimator to some optimal estimator when the available data goes to infinity.
Abstract: We analyze the problem of estimating product variables from process measurements in manufacturing systems. In particular, a novel approach for studying the performance of such estimators in terms of their expected performance is introduced. Using ideas from statistical learning theory, we obtain sufficient conditions on the manufacturing process, the estimation algorithm, and the design procedure to guarantee asymptotic convergence of the estimation algorithm to some optimal estimator when the available data goes to infinity.
2 citations
01 Jan 2007
TL;DR: The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state ofDTG.
Abstract: Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.
2 citations
18 Nov 2010
TL;DR: A new power transformer fault diagnosis method based on support vector machine, which has many advantages for transformer faults diagnosis, such as simple algorithm, good classification and high efficiency.
Abstract: The power transformer is very important equipment in a power system, and it is necessary to carry through faults diagnosis for it. Support vector machine is a machine learning algorithm based on statistical learning theory, which can get good classification effects with a few learning samples. A new power transformer fault diagnosis method based on support vector machine is presented in this paper. The method has many advantages for transformer faults diagnosis, such as simple algorithm, good classification and high efficiency. This faults diagnosis method finally has been proved by many practical faults data of power transformer. Compared experiment results with the traditional three-ratio method, this method has higher diagnosis right ratio. So it shows that such method is very feasibile and is very suitable for power transformer faults diagnosis.
2 citations
01 Dec 2018
TL;DR: Sahara as discussed by the authors is an efficient training algorithm for SVM, which identifies data points that have no influence on SVM classification by computing the upper and lower bounds of a parameter that determines the hyper-plane.
Abstract: Support Vector Machine (SVM) is one of the most popular classification algorithms. SVM separates data points into two classes by using the hyper-plane that is maximally distant from the two classes. Since SVM is theoretically based on statistical learning theory and the principle of structural risk minimization, it offers highly accurate classification. However, its training process is computationally expensive. This paper proposes Sahara as an efficient training algorithm for SVM. It identifies data points that have no influence on SVM classification by computing the upper and lower bounds of a parameter that determines the hyper-plane. Our approach can efficiently compute the bounds by using Singular Value Decomposition (SVD) and a sparse data matrix. Theoretically, our approach guarantees to yield the optimal hyper-plane of SVM for any given set of data points. Experiments show that Sahara is significantly faster than previous approaches.
2 citations
Posted Content•
12 Feb 2004
TL;DR: This work analyzes how abstract Bayesian learners would perform on different data and discusses possible experiments that can determine which learning–theoretic computation is performed by a particular organism.
Abstract: Advances in statistical learning theory leave us with many possible designs of learning machines. But which of them are implemented by brains, metabolic and genetic networks, and other biological information processors? We analyze how abstract Bayesian learners would perform on different data and discuss possible experiments that can determine which learning–theoretic computation is performed by a particular organism.
2 citations