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In Human Resource Management (HRM), the top challenge for HR professionals is managing the organizational talents. The talent management problem can be solved using the classification technique in data mining. There are several classification techniques present such as Decision Tree, Neural Networks, Support vector machine (SVM) and nearest neighbour algorithm. In this paper we suggest a combined...
With the high processing power of today's smartphones, it becomes possible to turn a smartphone into a personal audio surveillance and monitoring system. Ideally, such a system should be able to detect and classify a variety of sound events 24 hours a day and trigger an emergence phone call or message once a specified sound event (e.g., screaming) occurs. To prolong battery life, it is important to...
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...
Since 1985, the IEEE 754 standard defines formats, rounding modes and basic operations for floating-point arithmetic. In 2008 the standard has been extended, and recommendations have been added about the rounding of some elementary functions such as trigonometric functions (cosine, sine, tangent and their inverses), exponentials, and logarithms. However to guarantee the exact rounding of these functions...
In this paper the concepts of controllability and zero-controllability of a (to be controlled) variable w through a (control) variable c are introduced and characterized. By assuming this perspective, the dead-beat control (DBC) problem is posed as the problem of designing a controller such that, for the resulting controlled behavior, the to be controlled variable w goes to zero in a finite number...
The face recognition system consists of a feature extraction step and a classification step. In this paper, the researcher studies the use of linear and nonlinear methods for feature extraction in the face recognition system. The linear Principal component analysis (PCA) which is widely used in the face recognition is used to construct the feature space and extract features. The Kernel-PCA is extended...
An improved feature-level fusion algorithm based on kernel canonical correlation analysis is presented and applied to multimodal recognition based on fusion of ear and profile face in this paper. The fusion of ear and face biometrics could fully utilize their connection relationship of physiological location, and possess the advantage of recognizing people without their cooperation. First, only the...
This paper proposes a new behavioral PA modeling scheme which combines existing pruned Volterra (PV) model with infinite impulse response (IIR) basis functions. These IIR basis functions are inherently composed of a parallel structure of single-pole IIR filters, summed at the output. Performance of this new IIR-PV model is found to be significantly better than existing memory polynomial (MP) and pruned...
The plain chest radiograph is the basic tool in diagnosis and follow-up patients in general times with inexpensive imaging and to demonstrate pathological lesions. Selection an optimum kVp and mAs for the thickness of the Patient can give a qualified radiography and accurate diagnosis. The aim of this study is designing the mathematical model in polynomial equation by using kernel function and least...
This paper deals with the observer problem for dynamical systems in a behavioral context. We are given a dynamical system together with a partition of the system variables into a set of known or measured variables and a set of unknown, to be estimated variables. The observer problem is to find a system that produces an estimate of the unknown variables on the basis of the known or measured variables...
In this paper we consider the LQR control problem with no penalty on the input; this is addressed in the literature as the singular LQR control problem. We show that here the optimal controller is no longer a static controller but a PD controller. We also show that the closed loop system, i.e. the controlled system is a singular state space system. Singular system brings in the concern of existence...
The identification of multiple affine subspaces from a set of data is of interest in fields such as system identification, data compression, image processing and signal processing and in the literature referred to as subspace clustering. If the origin of each sample would be known, the problem would be trivially solved by applying principal component analysis to samples originated from the same subspace...
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...
The selection and design of appropriate kernel functions play a key role in effective support vector machine (SVM) leaning. A general strategy is to customize the existent kernel functions to fit into the data property and structure. Wavelet kernels have been developed to approximate arbitrary nonlinear functions for signal processing. In this paper, we propose novel wavelet kernels based on the Riemannian...
In the present paper the linear feedback equivalence problem is addressed for time delay systems. It is shown that thanks to the use of new mathematical tools recently introduced in the literature for dealing with time delay systems, it is possible to define necessary and sufficient conditions for the solvability of the problem.
This paper presents an application of Support Vector Machine (SVM) for digital protection of power transformer which effectively discriminates internal faults with non-internal faults. Internal faults consist of phase to ground, phase to phase and phase to phase to ground faults, whereas non-internal faults include different types of magnetising inrush, external fault & normal condition. The existing...
Image recognition technologies have been used in many areas, and feature extraction of image is key step for image recognition. A novel feature extraction method using kernel self-optimized learning for image recognition. The scheme of image feature extraction includes textural extraction using Gabor wavelet, textural features reduction based on class-wise locality preserving projection with...
This paper describes the classification of infant cry with asphyxia using orthogonal least square based support vector machine with polynomial kernel. Optimization of input feature set and filter bank number of mel frequency cepstrum coefficient were performed to produce accurate results. These input feature sets were classified using support vector machine (SVM) with polynomial kernel. To enhance...
We present a new Coprime Blurred Pair (CBP) theory that may benefit a number of computer vision applications. A CBP is constructed by blurring the same latent image with two unknown kernels, where the two kernels are co-prime when mapped to bivariate polynomials under the z-transform. We first show that the blurred contents in a CBP are difficult to restore using conventional blind deconvolution methods...
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