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For a support vector algorithm, the problem of sensitivity to noise points is considered as one of the major problems that may affect the accuracy of the results. In this paper, a weighted method based on rough neighborhood approximation is proposed to reduce the influence of noise points for support vector data description algorithm, which is an important branch of support vector model. Based on...
Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings...
Almost all multi-target tracking systems have to generate point estimates for the targets, e.g., for displaying the tracks. The novel idea in this paper is to consider point estimates for multi-target states that are optimal according to a kernel distance measure. Because the kernel distance is a metric on point sets and ignores the target labels, shortcomings of Minimum Mean Squared Error (MMSE)...
The tradeoff between noise reduction and speech distortion is a key concern in designing noise reduction algorithms. We have proposed a regularization framework for noise reduction with the consideration of the tradeoff problem. We regard speech estimation as a functional approximation problem in a reproducing kernel Hilbert space (RKHS). In the estimation, the objective function is formulated to...
Approximate Bayesian computation (ABC) is a class of simulation-based statistical inference procedures that are increasingly being applied in scenarios where the likelihood function is either analytically unavailable or computationally prohibitive. These methods use, in a principled manner, simulations of the output of a parametrized system in lieu of computing the likelihood to perform parametric...
We propose a full-state feedback law to stabilize linear first-order hyperbolic systems featuring n positive and one negative transport speeds on a finite space domain. Only one state, corresponding to the negative velocity, is actuated at the right boundary. The proposed controller guarantees convergence of the whole (n + 1)-state system to zero in the L2-sense.
Blood microscopic image segmentation is a fundamental tool for automated diagnosis of hematological disorders. In particular, lymphoblast image segmentation acts as the foundation for all image based leukemia diagnostic system. Precision in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented...
Owing to the large scale of multi-dimensional datasets in image processing, the standard Support Vector Machine (SVM) has a high time complexity in the training process for image segmentation. A new machine learning method, Ball Vector Machine (BVM) is used for image segmentation in order to reduce the training time in this paper. The experimental results show that BVM has a similar segmentation effect...
As vast environmental monitoring projects continue to proliferate, the problem of efficient data representation becomes more and more significant. We tackle the fundamental question of what is the limit of lossy compression of a data stream under the L∞ norm. We describe a method to compute a conservative estimate of the entropy of a sequence of non-independent random variables underlying a data stream...
Completion or imputation of three-way data arrays with missing entries is a basic problem encountered in various areas, including bio-informatics, image processing, and preference analysis. If available, prior information about the data at hand should be incorporated to enhance performance of the imputation method adopted. This is the motivation behind the proposed low-rank tensor estimator which...
We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead...
PmSVM (Power Mean SVM), a classifier that trains significantly faster than state-of-the-art linear and non-linear SVM solvers in large scale visual classification tasks, is presented. PmSVM also achieves higher accuracies. A scalable learning method for large vision problems, e.g., with millions of examples or dimensions, is a key component in many current vision systems. Recent progresses have enabled...
Linear SVMs are efficient in both training and testing, however the data in real applications is rarely linearly separable. Non-linear kernel SVMs are too computationally intensive for applications with large-scale data sets. Recently locally linear classifiers have gained popularity due to their efficiency whilst remaining competitive with kernel methods. The vanilla nearest neighbor algorithm is...
Efficient learning with non-linear kernels is often based on extracting features from the data that “linearise” the kernel. While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse features for arbitrary kernels, re-deriving as a special case a popular map for the intersection kernel and...
This paper extends the classical warping-based optical flow method to achieve accurate flow in the presence of spatially-varying motion blur. Our idea is to parameterize the appearance of each frame as a function of both the pixel motion and the motion-induced blur. We search for the flows that best match two consecutive frames, which amounts to finding the derivative of a blurred frame with respect...
Genome-wide analysis of single nucleotide polymorphisms (SNP) can potentially be helpful in exploring the role of genetic variability in drug therapy. However, two major problems with such an analysis are the need for a large number of interrogated genomes, and the resulting high-dimensional data where the number of SNPs used as features is much larger than the number of subjects. The aim of this...
This paper deals with fast vanishing point estimation for autonomous robot navigation. Preceding approaches showed suitable results and vanishing point estimation was used in many robotics tasks, especially in the detection of illstructured roads. The main drawback of such approaches is the computational complexity - the possibilities of hardware accelerations are mentioned in many papers, however,...
We propose a region-based segmentation method based on local statistics. The adaptive spatial locality is defined using the Intersection of Confidence Intervals (ICI) approach. This pixel dependent local scale is estimated, conditionally on the current segmentation, in the sense of minimizing the mean-square error of a Local Polynomials Approximation (LPA). In other words, the scale is ‘optimal’ since...
Data smoothing is an important step within a data processing procedure that allows one to stress the most important pattern of a function relation between a studied object and given variables. Recently, Holčapek and Tichý (2011) suggested a smoothing filter based on fuzzy transform approach of Perfilieva (2004) and compared it to Nadaraya-Watson estimator. However, within the analysis only one independent...
The particle Gibbs (PG) sampler was introduced in [1] as a way to incorporate a particle filter (PF) in a Markov chain Monte Carlo (MCMC) sampler. The resulting method was shown to be an efficient tool for joint Bayesian parameter and state inference in nonlinear, non-Gaussian state-space models. However, the mixing of the PG kernel can be very poor when there is severe degeneracy in the PF. Hence,...
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