Smoothing methods, extensively used for solving important mathematical programming problems and applications, are proposed here to generate and solve an unconstrained smooth reformulation of support vector machines for pattern classification using completely arbitrary kernels. We term such reformulations smooth support vector machines (SSVMs). A fast Newton-Armijo algorithm for solving the SSVM...

The paper presents a decision support system for heart diseases classification based on support vector machines (SVM) and integer-coded genetic algorithm (GA). Simple Support Vector Machine (SSVM) algorithm has been used to determine the support vectors in a fast, iterative manner. For selecting the important and relevant features and discarding those irrelevant and redundant ones, integer-code...

In this paper, a hybrid system was proposed for chemical entity mention recognition (CEMP) and gene/protein related object recognition (GPRO) in BeCalm challenge. Firstly, five individual machine learning-based subsystems were developed to identify chemical and gene/protein related entity mentions, that is, a bidirectional LSTM (long-short term memory, a variant of recurrent neural network)-bas...

Tackling problems in areas such as computer vision, bioinformatics, speech or text recognition is often done best by taking into account task-specific statistical relations between output variables. In structured prediction, this internal structure is levered to predict multiple outputs simultaneously, leading to more accurate and coherent predictions. Structural support vector machines (SSVMs)...

Semi-supervised Support vector machine has become an increasingly popular tool for machine learning due to its wide applicability. Unlike SVM, their formulation leads to a non-smooth non-convex optimization problem. In 2005, Chapelle and Zien used a Gaussian approximation as a smooth function and presented ∇TSVM. In this paper, we propose a smooth piecewise function and research smooth piecewis...

‎in this paper‎, ‎we introduce the structure of a groupoid associated to a vector field‎ ‎on a smooth manifold‎. ‎we show that in the case of the $1$-dimensional manifolds‎, ‎our‎ ‎groupoid has a‎ ‎smooth structure such that makes it into a lie groupoid‎. ‎using this approach‎, ‎we associated to‎ ‎every vector field an equivalence‎ ‎relation on the lie algebra of all vector fields on the smooth...

Support Vector Machine (SVM) is one of the most popular classifiers in pattern recognition, which aims to find a hyperplane that can separate two classes of samples with the maximal margin. As a result, traditional SVM usually more focuses on the scatter between classes, but neglects the different data distributions within classes which are also vital for an optimal classifier in different real...

The quadratically convergent algorithms for training SVM with smoothing methods are discussed in this paper. By smoothing the objective function of an SVM formulation, Lee and Mangasarian [Comput Optim Appl 20(1):5-22, 2001] presented one such algorithm called SSVM and proved that the error bound between the new smooth problem and the original one was O(1/p) for large positive smoothing paramet...