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With the rapid development of society and technology, home service robot is becoming cheaper and smarter. Facing with the difficulties of aging and shortage of labor, we can use home service robot (HSR) as a good companion and servant. However, the security and reliability problems have become bottlenecks in this field. It is meaningful to do researches on fault diagnosis of HSR. Due to its excellent...
Least squares support vector machines (LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method...
Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented. For different applications, different kernels can be chosen and configured...
Feature selection and parameters optimization is an important step in the using of SVM. In recent years, more researchers are mainly focus in feature selection and parameters optimization. However, the number of support vectors with the selected support vector subset also has an effect on classification performance of SVM. Few researchers concentrate on this area. This paper proposed a novel optimization...
In order to solve multi-class classification problem in real world, we improved TSVM in this paper. We combined LSTSVM with partial binary tree to improve classification efficiency. Binary tree hierarchy can solved the inseparable regional issues in OVO-SVM and OVA-SVM classification. Experimental results show that it improved the classification accuracy. It also has better speed-up ratio than the...
In this paper, a study of the parallel exploitation of a Support Vector Machine (SVM) classifier with a linear kernel running on a Massively Parallel Processor Array platform is exposed. This system joins 256 cores working in parallel and grouped in 16 different clusters. The main objective of the research has been to develop an optimal implementation of the SVM classifier on a MPPA platform whilst...
A novel method called boundary distance is proposed for pre-extracting support vectors. It first calculates the distance between the sample and the other class sample. According to sort distance, the less distance samples, and nearest neighbor samples in the other class, are used as boundary samples. As the boundary samples include most support vectors, it greatly declines the training time without...
Personal emotions accompany us in our daily life, affecting our learning and work, therefore it is necessary to obtain better understanding of human behavior through emotional assessment. This paper proposes a method for recognizing emotions electroencephalography(EEG) based on relevance vector machine(RVM). Emotional states of two types as positive and negative were selected from a standard database...
In order to reduce the computational complexity of kernel machines and improve their performance in multi-label classification, we develop a systematic two step batch approach for constructing and training a new multiclass kernel machine (MKM). The proposed paradigm prunes the kernels, and uses Newton's method to improve the kernel parameters. In each iteration, output weights are found using orthogonal...
Support Vector Data Description (SVDD) has a limitation for dealing with a large dataset or online learning tasks. This work investigates the practice of credit scoring and proposes a new incremental learning algorithm for SVDD based on Karush-Kuhn-Tucker (KKT) conditions and convex hull. Convex hull and part of newly added samples which violates KKT conditions are treated as new training samples...
SVM classifiers with Half Against Half (HAH) architecture are reported to be the fastest classifier amongst other SVM classification architectures reported in literature. An attempt is made to enhance the speed of HAH SVM classifier and is named as Fast HAH (F-HAH) classifier. The performance of proposed F-HAH classifier is evaluated using speaker dependent and multi-speaker dependent isolated digits...
A novel classifier architecture is introduced and its performances are evaluated against state of the art shallow classifiers. Its main advantage consists in a very fast learning ensured by a novelty detection algorithm, selecting a list of prototypes among the training samples, used as centers in a radial basis functions neurons layer. Only the radius of the basis functions is optimized to improve...
Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator spike detection, and a boosted cascade, multiclass kernel...
In pattern classification or machine learning, instance-based learning (IBL) has gained much attention and can yield superior performance in many domains. In IBL, however, the storage requirement is proportional to the number of training instances. Furthermore, it usually takes too much time to classify a new, unseen instance because all training instances need to be considered in determining the...
To make the traditional support vector data description (SVDD) achieve better generalization performance and more robust against noise, a selective ensemble method based on correntropy and Renyi entropy is proposed. In this proposed ensemble method, the correntropy between the radii of the basis classifiers and the radius of the ensemble is utilized to substitute the sum-squared-error (SSE) criterion...
Support vector machine (SVM) is a machine learning method developed in the mid-1990s based on statistical learning theory. SVM classifier is currently more popular classifier. This paper presents a boundary detection technique for retaining the potential support vector. Through seeking to structural risk minimization of the SVM, it improves the learning generalization ability and achieves the minimization...
Recently, a customer review of travel information website has been a big influence on users in accommodations by the spread of the internet. In our research, as preprocessing of picking out information from the reviews, we propose a method to classify sentences into "Opinion sentences" and "Fact sentences" using SVM. And we confirm effectiveness of "opinion sentences"...
This paper studies a new method for identifying the new words, Objective to identify new words better. Method is first to extract the positive and negative samples from training corpus which was handled by segmentation and POS Tagging according to the dictionary, then combining with all kinds of words classification which was gotten from training corpus, and gaining the new word support vector through...
Supervised leaning classifier is usually constructing based on models through learning to achieve high accuracy. Support Vector Machine (SVM) is more useful classification technique in supervised learning model. In this paper, we examined SVM with linear kernel function and pre-computed kernel function using micro array data sets. In this observation is focused major three aspects such as accuracy,...
Sparse representation for classification (SRC) has attracted much attention in recent years. It usually performs well under the following assumptions. The first assumption is that each class has sufficient training samples. In other words, SRC is not good at dealing with the undersampled problem, i.e., each class has few training samples, even single sample. The second one is that the sample vectors...
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