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.

Fault Detection of Oil Pump Based on Classify Support Vector Machine

Abstract: Statistical learning theory is introduced to fault detection of oil pump. Considering the issues that the relationship between the fault of oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The support vector machine (SVM) has the ability of strong nonlinear function approach and the ability of strong generalization and also has the feature of global optimization. In this paper, a fault detection method of oil pump based on SVM is presented, moreover, the genetic algorithm(GA) was used to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the detection method can truly diagnosticate the fault of oil pump by learning the fault information of oil pump. The real detection results show that this method is feasible and effective.

Applying a kernel function on time-dependent data to provide supervised-learning guarantees

Abstract: We employ a Monte-Carlo approach to find the best phase space for a given data stream.We propose kFTCV, a novel approach to validate data stream classification.Results show Taken's theorem can transform data streams into independent states.Therefore, we can rely on SLT framework to ensure learning when dealing with data streams. The Statistical Learning Theory (SLT) defines five assumptions to ensure learning for supervised algorithms. Data independency is one of those assumptions, once the SLT relies on the Law of Large Numbers to ensure learning bounds. As a consequence, this assumption imposes a strong limitation to guarantee learning on time-dependent scenarios. In order to tackle this issue, some researchers relax this assumption with the detriment of invalidating all theoretical results provided by the SLT. In this paper we apply a kernel function, more precisely the Takens' immersion theorem, to reconstruct time-dependent open-ended sequences of observations, also referred to as data streams in the context of Machine Learning, into multidimensional spaces (a.k.a. phase spaces) in attempt to hold the data independency assumption. At first, we study the best immersion parameterization for our kernel function using the Distance-Weighted Nearest Neighbors (DWNN). Next, we use this best immersion to recursively forecast next observations based on the prediction horizon, estimated using the Lyapunov exponent. Afterwards, predicted observations are compared against the expected ones using the Mean Distance from the Diagonal Line (MDDL). Theoretical and experimental results based on a cross-validation strategy provide stronger evidences of generalization, what allows us to conclude that one can learn from time-dependent data after using our approach. This opens up a very important possibility for ensuring supervised learning when it comes to time-dependent data, being useful to tackle applications such as in the climate, animal tracking, biology and other domains.

Robustness of support vector machine-based classification of heart rate signals.

Abstract: In this study, we discuss the use of Support Vector Machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters can be used for the categorization of new signals not belonging to the training set. We have experimented with both real and artificial signals and the SVM classifier performs very well even with signals exhibiting very low signal to noise ratio which is not the case for other standard methods proposed by the literature. I. INTRODUCTION Heart Rate Variability (HRV) analysis is based on measur- ing the variability of heart rate signals and more specifically, the variability in intervals between R peaks of the electrocar- diogram (ECG), referred as RR intervals. Several techniques have been proposed for the investigation of evolution of features of the HRV time series. A survey of statistical methods, based on the estimation of the statistical properties of the beat-to-beat time series, can be found in (1). These methods describe the average statistical behavior of the signal over a considered time window. Spectral methods (2), based on FFT or standard autoregressive modeling, were also proposed. More recently, nonlinear approaches, including Markov modeling (3), entropy-based metrics (4), (5), the mutual information measure (6) and probabilistic modeling (7), (8) were presented to examine heart rate fluctuations. Other methods include the application of the Karhunen- Lo¨ eve transformation (9) or modulation analysis (10), (11). In this study, we investigate the potential benefit of using a support vector machine (SVM) learning (12), (13) to classify heart rate signals. Support vector classifiers are based on recent advances on statistical learning theory (14). They use a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from opti- mization theory that implements a learning bias derived from statistical learning theory. In the last decade, SVM learning has found a wide range of applications (15), including image segmentation (16) and classification (17), object recognition (18), image fusion (19) and stereo correspondence (20). Based on our previous work on support vector classification

Application research of support vector machine in E-Learning for personality

Abstract: In order to accurately build the learner's learning style in E-Learning, according to the needs and preferences to provide personalized learning materials and harmonious human-computer interaction environment. This paper combines Felder-Silverman learning style with support vector machine technology, and use machine learning technologies for learners to build dynamic learning style. Through the analysis of the Emotion and recognition interaction of the personalized E-Learning based on statistical learning theory and support vector machine technology, it demonstrates the correctness and feasibility using support vector machine to build learning styles. The combination of support vector machine, emotion and recognition interaction in the personalized E-Learning makes great contribution to build human-computer interaction environment.