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Research that explores the use of machine learning for automatic security classification of information objects is about to emerge. In this paper we investigate the opportunity to increase the machine learning performance by taking advantage from time information that is "hidden" in the documents of the training set. This paper presents a technique to do so, and confirms that this is a promising...
Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it much...
Many state-of-the-art i-vector based voice biometric systems use linear discriminant analysis (LDA) as a post-processing stage to increase the computational efficiency in the back-end via dimensionality reduction, as well as annihilate the undesired (noisy) directions in the total variability subspace. The traditional approach for computing the LDA transform uses parametric representations for both...
In many spoken language understanding systems (SLUS), domain classification is the most crucial component, as system responses based on wrong domains often yield very unpleasant user experiences. In multi-lingual domain classification, the training data for some poor-resource languages often comes from machine translation. Some of the higher order n-gram features are distorted during machine translation...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
Biometric systems accurately recognise/authenticate an individual to access his confidential data/accounts. When multiple traits are fused together at feature/ score/ decision level, it results into highly accurate multimodal systems. This system improvise rate of recognizing an individual. Multiple biometric traits cannot be cloned simultaneously and hence it is highly secured system. The match scores...
Speaker age recognition is an essential technique in automation speech recognition based on the speech wavform parameters in speaker's voice. However, there are several challenges in speaker age recognition, such as innate differences in speaker's voice, subjective classification fuzzy, etc. The issue of speaker age based on isolated words is proposed in this paper, including support vector machine...
Machine Learning methods such as Neural Network (NN) and Support Vector Regression (SVR) have been studied extensively for time series forecasting. Multiple Kernel Learning (MKL) which utilizes SVR as the predictor is yet another recent approaches to choose suitable kernels from a given pool of kernels by means of a linear combination of some base kernels. However, some literatures suggest that this...
Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of...
Air pollution is an olfactory pollution because many polluting gases have a strong odor even at low concentrations. These pollutants are natural or anthropogenic emission sources. This pollution has many harmful effects on human health or upon the environment. So it is necessary to detect the pollution to reduce its effects. An electronic nose is capable of detecting the presence of gas after learning...
In conventional text categorization algorithms, documents are symbolized as “bag of words” (BOW) with the fact that documents are supposed to be independent from each other. While this approach simplifies the models, it ignores the semantic information between terms of each document. In this study, we develop a novel method to measure semantic similarity based on higher-order dependencies between...
Jamu is made from natural materials such as roots, leaves, timber and fruits. Jamu has many variations of formula. The composition of Jamu formula is usually based on empirical data or personal experiences. Thus, the classification for the efficacy of Jamu based on its compositions of plants still remains an interesting task. The purpose of this research is to develop a classification system for Jamu...
The problem of diagnosing Pima Indian Diabetes from data obtained from the UCI Repository of Machine Learning Databases[6] is handled with a modified Support Vector Machine strategy. Performance comparison with previous studies is presented in order to demonstrate the proposed algorithm's advantages over various classification methods. The goal of the paper is to provide the grasp of a methodology...
Credit risk evaluation has become an increasingly important field in financial risk management for financial institutions, especially for banks and credit card companies. Many data mining and statistical methods have been applied to this field. Extreme learning machine (ELM) classifier as a type of generalized single hidden layer feed-forward networks has been used in many applications and achieve...
Support vector machines (SVMs) are probably the most well-known models based on kernel substitution. Based on orthogonal Legendre polynomials, an orthogonal Legendre kernel function for support vector machine is proposed using the properties of kernel functions. We then prove that it satisfies the Mercer condition. Compared to traditional kernel functions such as polynomial or gaussian kernels, orthogonal...
EEG data contains high-dimensional data that requires considerable computational power for distinguishing different classes. Dimension reduction is commonly used to reduces the necessary training time of the classifiers with some degree of accuracy lost. The dimension reduction is usually performed on either feature or electrode space. In this study, a new dimension reduction method that reduce the...
In this paper, we propose a discriminative method for the acoustic feature based language recognizer, which is a modification of the polynomial expansion in generalized linear discriminant sequence (GLDS) kernel. It is inspired by the Gaussian mixture model-support vector machine (GMM-SVM) system which has been successfully used in both speaker and language recognition. Because of the restriction...
The paper presents a new binary classification method based on the minimization of the slack variables energy called the Mean Squared Slack (MSS). We deliver preliminary mathematical results which support the motivation behind our approach. We show that (a) in the linearly separable case the minimum MSS is attained at a separating vector, while (b) the minimizer in the linearly non-separable case...
This paper presents an overview of feature extraction and selection methods for recognition of numerals & characters(Devnagari). Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems.
The classification performance using support vector machines (SVMs) for transcriptomic analysis can be limited due to the high dimensionality of the data. This limitation is most problematic in the case of small training sets. A general solution is to employ a dimension reduction method before SVM classification. In this paper, we propose a novel singular value decomposition (SVD) based method for...
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