Support Vector Machines (SVM) is a new family of Machine Learning techniques that have been used in many areas showing remarkable results. Since training SVM scales quadratically (or worse) according of data size, it is worth to explore novel implementation approaches to speed up the execution of this type of algorithms. In this paper, a hardware-software architecture to accelerate the SVM trai...

The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization (NMF) problem. This allows us to derive a novel multiplicative algorithm for solving hard and sof...

We present a stream algorithm for large scale classification (in the context of l2-SVM) by leveraging connections between learning and computational geometry. The stream model [1] imposes the constraint that only a single pass over the data is allowed. We study the streaming model for the problem of binary classification with SVMs and propose a single pass SVM algorithm based on the minimum enc...

Twitter has become one of the most popular micro-blogging platform, recently. Millions of users can share their thoughts and opinions about various aspects and activites. Therefore, twitter considered as a rich source of information for decision-making and sentiment analysis. In this case, the sentiment is aimed to overcome the problem of automatically classifying user tweets into positive opin...

An algorithm based on support vector machines (SVM), the most recent advance in pattern recognition, is presented for use in classifying light-induced autofluorescence collected from cancerous and normal tissues. The in vivo autofluorescence spectra used for development and evaluation of SVM diagnostic algorithms were measured from 85 nasopharyngeal carcinoma (NPC) lesions and 131 normal tissue...

In this paper, we present a novel method for reducing the computational complexity of a Support Vector Machine (SVM) classifier without significant loss of accuracy. We apply this algorithm to the problem of face detection in images. To achieve high run-time efficiency, the complexity of the classifier is made dependent on the input image patch by use of a Cascaded Reduced Set Vector expansion ...

Abstract: Icing on power transmission lines is a serious threat to the security and stability of the power grid, and it is necessary to establish a forecasting model to make accurate predictions of icing thickness. In order to improve the forecasting accuracy with regard to icing thickness, this paper proposes a combination model based on a wavelet support vector machine (w-SVM) and a quantum f...