Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classification problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership ...

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum ...

Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification problems with a large number of training data. To overcome this problem, in this paper, we discuss extracting boundary data from the training data and train the support vector machine using only these data. Namely, for ...

In this paper, we propose a combined method for hand shape recognition. It consists of support vector machines (SVMs) and an online learning algorithm based on the perceptron. We apply HOG features to each method. First, our method estimates a hand shape of an input image by using SVMs. Here the online learning method with the perceptron uses the input image as new training data if the data is ...

In this paper, facial age estimation is discussed in a novel viewpoint – how to jointly exploit the supervised training data and human annotations to improve the age estimation precision. This is motivated by the lacking of data problem in age estimation and the current web booming. To do so, fuzzy age label is firstly defined, and it is then merged into the Support Vector Regression (SVR) fram...

This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at e...

We develop an intuitive geometric framework for support vector regression (SVR). By examining when ǫ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard and soft ǫ-tubes are constructed by separating the convex or reduced convex hulls respectively of the training data with the response variable shifted up and down by ǫ. A novel SVR model is proposed...

Linear regression (LR) and support vector regression (SVR) are widely used in data analysis. Geometrical correlation learning (GcLearn) was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation). This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR...