This paper describes a hybrid model and the corresponding algorithm combining support vector machines (SVMs) with statistical methods to improve the performance of SVMs for the task of Chinese Named Entity Recognition (NER). In this algorithm, a threshold of the distance from the test sample to the hyperplane of SVMs in feature space is used to separate SVMs region and statistical method region...

This paper introduces the Clifford Support Vector Machines as a generalization of the realand complexvalued Support Vector M* chines. The major advantage of this approach is that one requires only one CSVM which can admit multiple multivector inputs and it can carry multi-class classification. In contrast one would need many real valued SVMs for a multi-class problem which is time consuming.

This paper investigates a novel application of support vector machines (SVMs) for sentence reduction. We also propose a new probabilistic sentence reduction method based on support vector machine learning. Experimental results show that the proposed methods outperform earlier methods in term of sentence reduction performance.

Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector regression (SVR) in the primal, thus providing for us scalable, sparse support vector regression algorithm, named SSVR-SRS. Empirical comparisons show that the number of basis functions required by the proposed algor...

Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our way we prove several results which are of great importance for the understanding of SVMs. In parti...

Support vector machines (SVMs) have gained great attention and have been used extensively and successfully in the field of sounds (events) recognition. However, the extension of SVMs to real-world signal processing applications is still an ongoing research topic. Our work consists of illustrating the potential of SVMs on recognizing impulsive audio signals belonging to a complex realworld datas...

Through task decomposition and module combination, minmax modular support vector machines (M-SVMs) can be successfully used for difficult pattern classification task. M-SVMs divide the training data set of the original problem to several sub-sets, and combine them to a series of sub-problems which can be trained more effectively. In this paper, we explore the use of M-SVMs in multi-view face re...

In this paper, different models of the pressure buildup inside a hydraulic servoaxis are compared. These models are obtained using RBF networks, local linear models and support vector machines (SVMs), with a particular focus on the latter. For SVMs, a reduction method is derived, which allows to reduce the number of support vectors without losing the generalization abilities of the SVM. Experim...

Support Vector Machines (SVMs) are a machine learning method rooted in statistical learning theory. One of their most interesting characteristics is that the solution achieved during training is sparse, meaning that a few samples are usually considered “important” by the algorithm (the so-called support vectors) and give account of most of the complexity of the classification/regression task. I...

Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation betwee...