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This paper proposes an framework to create the prediction model for energy consumption of mobile phone battery using data mining, based on three usage patterns of the phone: the standby state, video playing, and web browsing. In this model, the battery discharge rate are analyzed and used for constructing the model. To predict the power used, the perception neural network and support vector machine...
Many interesting papers have been written in the recent past on kernel clustering and many attractive results have also been demonstrated. Here we question the rationality behind such clustering approaches. Using simple data sets we argue and demonstrate that it is not a good idea to find clusters in the kernel space when the objective is to look for clusters in the original data because in the kernel...
Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector Machine...
In this paper, the Volterra series decomposition of a class of time invariant system, polynomial in the state and affine in the input, with an exponentially stable linear part is analyzed. A formal recursive expression of Volterra kernels of the input-to-state system is derived and the singular inversion theorem is used to prove the non-local-in-time convergence of the Volterra series to a trajectory...
We show how to compute a minimal Riccati-balanced state map and a minimal Riccati-balanced state space representation starting from an image representation of a strictly dissipative system. The result is based on an iterative procedure to solve a generalization of the Nevanlinna interpolation problem.
Given a nominal plant, together with a fixed neighborhood of this plant, the problem of robust stabilization is to find a controller that stabilizes all plants in that neighborhood (in an appropriate sense). If a controller achieves this design objective, we say that it robustly stabilizes the nominal plant. In this paper we formulate the robust stabilization problem in a behavioral framework, with...
Dissipative systems have played an important role in the analysis and synthesis of dynamical systems. The commonly used definition of dissipativity often requires an assumption on the controllability of the system. However, it is very natural to think of Lyapunov functions as storage functions for autonomous systems with power supplied to the system equal to zero. We use a definition of dissipativity...
In the computation process of support vector machine (SVM), one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overcome computational and storage problem for large-scale data set, a new framework, Support Matrix Machine...
This paper presents an optimized solution to peak to average power (PAPR) reduction system for hardware mapping. One of the critical issues of systems utilizing orthogonal frequency division multiplexing (OFDM) is the high PAPR of OFDM signals. PAPR reduction schemes increases the computational complexity of high throughput systems. The proposed optimized solution uses common subexpression elimination...
Automated text analysis and mining tools designed to identify the main topics of texts, chat room discussions, and web postings are an increasingly active research area due to the rapid explosion of Web information. This paper applies the nonlinear kernel-based Feature Vector Selection (FVS) approach followed by a Linear Discriminant Analysis (LDA) step to categorize unstructured text documents. Results...
Improving the precision of shot boundary detection is very important. This paper presents an algorithm for shot boundary detection based on SVM (support vector machine) in compressed domain. It uses the features, such as the type of macroblock, the difference between DC coefficients of two co-located blocks in successive frames and the type of frame, to segment a video into the shots by classifying...
Using the Chebyshev nodes and methods in reference, we established the estimation of covering number of learning theory in reproducing kernel Hilbert space. A counter example is presented to show that the estimation of covering number of Gaussian kernel functions.
In the process of image recognition, moment is an important method. Invariant moment is a kind of image recognition method by extracting the translation, rotation and scale invariant features of the images. The paper is mainly dedicated to the rotation invariant feature analysis of the Pseudo-Zernike moment and proposes a modified 1-iterative algorithm for computing the Pseudo-Zernike moments. The...
Previous research to improve the performance of Internet search engines has focused on classifying questions, sentences and user-goals but not the classification of sentences and phrases based on query intention and non-query intention. This paper investigates a classification system of query intention and non-query intention of sentences and phrases by firstly analyzing previous work and based on...
Let D = (V,A) be a digraph. A kernel of D is an independent set S of vertices such that every vertex of D is either in S or dominates a vertex in S. If every induced subdigraph of D has a kernel, then D is called kernel-perfect. According to Richardson, if a digraph does not contain a directed cycle of odd length then it is kernel-perfect. Here we study the kernel-perfection through use of the push...
This paper studies the design problem for the multi response linear model with possible bias. It is assumed that the fitted model for each response is polynomial of degree up to two, and the model bias includes the effects due to higher degree terms of multivariate Hermite polynomials. A criterion for choosing designs is proposed based on averaging the mean squared error over all possible bias. Several...
In order to overcome the dimension problem of the traditional fuzzy clustering, we use kernel-based fuzzy c-means clustering (KFCM) to construct first-order TSK fuzzy models. The proposed algorithm is composed of two phases. In the first phase, the antecedent fuzzy sets are obtained by KFCM. We present the expression of the cluster prototypes of KFCM with different kernel functions in original input...
We present a method for the synthesis of non-linear ranking function of a program loop. Based on the region-based search, it reduces the non-linear ranking function discovering to the inequality checking. The inequality prover BOTTEMA then can be utilized to check validity for inequalities. In contrast to other approaches, the new approach can also discover the ranking function with the radicals due...
Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have recently been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of simple statistical models. In this work, we have developed a predictive model based...
The kernel-based clustering has attracted great attention with the development of support vector machine. One can perform a clustering approach in an image space after mapping the data in an original space to the image space, but it is difficult to capture the optimal parameters for finding real clusters. In this paper, we present a kernel-based clustering approach in light of a relational fuzzy clustering...
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