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Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the...
This paper presents a method to estimate a depth map using an infrared projector and a pair of infrared color cameras that can capture infrared and color images simultaneously. The infrared projector projects a dot pattern so that the cameras capture infrared images of a scene textured by the dots with which depths to surfaces in the scene can be estimated regardless of whether they have visible textures...
Network traffic classification plays an extremely important role in network management and service. Support vector machine (SVM) is widely adopted to classify traffic flows for its high accuracy. All features selected are treated equally in traditional SVM network traffic classification, which take little consideration of that each feature exerts a different influence on classification. Therefore,...
We present Mixture of Support Vector Data Descriptions (mSVDD) for one-class classification or novelty detection. A mixture of optimal hyperspheres is automatically discovered to describe data. The model consists of two parts: log likelihood to control the fit of data to model (empirical risk) and regularization quantizer to control the generalization ability of model (general risk). Expectation Maximization...
Linear Support Vector Machine (LSVM) has recently become one of the most prominent learning methods for solving classification and regression problems because of its applications in text classification, word-sense disambiguation, and drug design. However LSVM and its variations cannot adapt accordingly to a dynamic dataset nor learn in online mode. In this paper, we introduce an Adaptable Linear Support...
The comparison of two classifiers, the Extreme Learning Machine (ELM) and the Support Vector Machine (SVM) is considered for performance, resources used (neurons or support vector kernels) and computational complexity (speed). Both implementations are of similar type (C++ compiled as Octave .mex files) to have a better evaluation of speed and computational complexity. Our results indicate that ELM...
Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary -- also known as hyperplane -- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute...
The development of the so-called intelligent tire has changed the role of the tire. Here we discuss a real-time road condition classification system that employs monitoring tire acceleration. Because the tire acceleration is non-stationary and is warped non-linearly in the time domain, we applied the time alignment algorithm to it similarly to speech recognition. High accuracy classification systems...
A collocation method is introduced for a class of Fred Holm integral equation of the second kind with weakly singular kernels. The key idea of this method is splitting the weakly singular kernel of the integral operator into finite parts so that the weak singularity is concentrated in one which can be analytically solved using integration by parts. Theoretical analysis and numerical examples show...
Study of the land cover classification using multi-source data are very important for eco-environment monitoring, land use planning and climatic change detection. In this study, the utility of multi-source RADARSAT-2 and LANDSAT-8 multi-spectral images for improving land cover classification performance using Support Vector Machine (SVM) classifier. HH polarized C band RADARSAT-2 images were fused...
This work focuses on automatic prediction of the writer's biometrics including gender, handedness and age information. The proposed prediction system is based on the use of Histogram of Oriented Gradients (HOG), which aims to extract gradient directions from the handwritten text. The prediction task is achieved using SVM classifier. Experiments performed on IAM and KHATT datasets, reveal promising...
The Method-of-Moment (MoM) discretization of the Electric-Field Integral Equation (EFIE) for a transversal electric (TE) illuminating plane wave impinging on an infinitely long cylinder (2D-object) traditionally requires continuous piecewise linear basis functions. These basis functions are particularly convenient in the numerical implementation because they reduce the degree of the Kernel singularity...
This paper proposes a new method for image registration by combining SURF and FREAK. SURF can extract robust feature points, and the topology of FREAK descriptor has strong ability of regional description. First, feature points of images are extracted by SURF, and described by FREAK descriptor. Then descriptors are roughly matched through the ratio of the closest neighbor and second closest one. Second,...
Thematic information detection is an important application of remote sensing image. Support vector machine (SVM) has been widely used in MODIS remote sensing detection. However, the difficulty of SVM application is how to select the suitable kernel function for remote sensing image. In this paper, the Sangeang Api volcanic ash cloud on May 30, 2014 is taken as an example, and the linear, polynomial,...
We offer an automated way of estimating the author of a song using only its lyrics content. To this end, we introduce a complete text classification framework which takes raw lyrics data as input and report estimated songwriter. The performance of the system is evaluated based on its classification and retrieval ability on a large dataset of Turkish songs, which was collected in this study. The results...
Efficient evaluation of near-singular surface integrals is important to the efficient implementation of the MoM. The focus here is on the Green function gradient kernel. A near-singularity cancellation quadrature scheme under present development (AD-R1-L-AS2) is compared to other three-subdomain splitting methods available in the literature. The main benefit of the simple three sub-triangle splitting...
Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion,...
We present a new approach for feature pooling in human action recognition. Instead of partitioning videos at predefined uniform intervals in a spatial-temporal volume as done with spatial pyramid matching, our method adaptively partitions in a pooling attribute space, defined by multiple trajectory-based cues. The pooling attributes include individual spatial and temporal coordinates of a trajectory,...
For the functioning of American democracy, the Lobbying Disclosure Act (LDA), for the very first time, provides data to empirically research interest groups behaviors and their influence on congressional policymaking. One of the main research challenges is to automatically find the topic(s), by short & sparse text classification, in a large corpus of unorganized, semi-structured, and poorly...
This paper addresses the problem of recognizing handwritten numerals for Gujarati Language. Three methods are presented for feature extraction. One belongs to the spatial domain and other two belongs to the transform domain. In first technique, a new method has been proposed for spatial domain which is based on Freeman chain code. This method obtains the global direction by considering n × n neighbourhood...
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