This paper proposes an idea for advanced learning system to school children using virtual character recognition for smart class rooms. The proposed technique has two phases, the first phase involves in creating numerals or character input virtually. The virtual input is obtained by tracking the movement of colored blob in front of the webcam. The blob can be detected with the help of Region of ...

Support Vector Machines present an interesting and effective approach to solve automated classification tasks. Although it only handles binary and supervised problems by nature, it has been transformed into multiclass and semi-supervised approaches in several works. A previous study on supervised and semi-supervised SVM classification over binary taxonomies showed how the latter clearly outperf...

Groundwater level prediction in a water basin plays a significant role in the management of groundwater resources. Aground water level forecasting system is developed in this study using Support vector Machines (SVM). Further Quantum behaved Particle Swarm Optimization (QPSO) function is employed in this study to determine the SVM parameters. Later, the proposed SVM-QPSO model is used in determ...

It is generally believed that the support vector machine (SVM) optimizes the generalization error and outperforms other learning machines. We show analytically, by concrete examples in the one dimensional case, that the SVM does improve the mean and standard deviation of the generalization error by a constant factor, compared to the worst learning machine. Our approach is in terms of the extrem...

Support Vector Machines(SVMs) have become more popular than other algorithms for pattern classification. The learning phase of a SVM involves exploring the subset of informative training examples (i.e. support vectors) that makes up a decision boundary. Those support vectors tend to lie close to the learned boundary. In view of nearest neighbor property, the neighbors of a support vector become...

Software defect prediction is an important aspect of preventive maintenance of a software. Many techniques have been employed to improve software quality through defect prediction. This paper introduces an approach of defect prediction through a machine learning algorithm, support vector machines (SVM), by using the code smells as the factor. Smell prediction model based on support vector machi...

Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM...

Hypertext/text domains are characterized by several tens or hundreds of thousands of features. This represents a challenge for supervised learning algorithms which have to learn accurate classifiers using a small set of available training examples. In this paper, a fuzzy semi-supervised support vector machines (FSS-SVM) algorithm is proposed. It tries to overcome the need for a large labelled t...

A new method for recognition of isolated handwritten Arabic/Persian digits is presented. This method is based on Support Vector Machines (SVMs), and a new approach of feature extraction. Each digit is considered from four different views, and from each view 16 features are extracted and combined to obtain 64 features. Using these features, multiple SVM classifiers are trained to separate differ...

Hypertext/text domains are characterized by several tens or hundreds of thousands of features. This represents a challenge for supervised learning algorithms which have to learn accurate classifiers using a small set of available training examples. In this paper, a fuzzy semi-supervised support vector machines (FSS-SVM) algorithm is proposed. It tries to overcome the need for a large labelled t...