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This paper introduces a multi-class classification algorithm based on sparse representation which considers on rejection option to minimize risks caused by outliers. Here the outliers include signals that do not belong to any classes learned in a training step. To successfully reject the outliers, new rejection measure and corresponding dictionary learning algorithm are presented. Experimental results...
The k-nearest neighbor classification method predicts the class label of a query pattern based on its nearest neighbors. So which samples can be selected as the nearest neighbors of the query pattern and how to use these neighbor samples to predict the class label of the query pattern are two key problems in the nearest neighbor based method. Based on the definition of mutual information distance...
Factor analysis is mainly by extracting the compact representations of speakers' utterances, which are referred to as i-vectors. A low new space called total variability space, which is speaker and channel dependent is trained in the modeling. During the experiments, channel compensation approaches are used to remove the interference included by i-vectors. They are respectively are Nuisance Attribute...
In this paper, a novel learning-based fuzzy Linear Quadratic Regulator (LQR) control method using Extended Kalman Filter (EKF) to optimize a Mamdani fuzzy LQR controller is presented. The EKF is used to adjust the shape of membership functions and rules of the fuzzy controller to adapt with the working conditions automatically during the operation process to minimize the control error. Then, the LQR...
We propose a novel low-dimensional discriminative dictionary learning approach for multi-class classification tasks, Latent Structure based Discriminative Dictionary Learning (LS-DDL). Our approach first projects features and class labels onto a shared latent structure space, and then generates a discriminative and low-dimensional input to a discriminative dictionary learning framework. LS-DDL learns...
By combining two fast training methods, i.e., the weights-direct-determination (WDD) method and Levenberg-Marquardt method, this paper proposes a novel training algorithm called weights and structure policy (WASP) for the three-layer feedforward neuronet, in addition to the algorithm of weights and structure determination (WASD). Note that the pruning-while-growing and second-pruning techniques are...
Cooperative networks recently became attractive because they can achieve spatial diversity. Multiple decode-and-forward (DF) relay nodes result in multiple carrier frequency offsets (CFOs) because each relay node owns its local oscillator. This paper presents a novel semi-blind multiple-CFO estimator for DF OFDM cooperative networks. A procedure is designed to effectively derive the semi-blind multiple-CFO...
In this study, we have classified well known 20 News Group Set that contains 20.000 documents with a Naïve Bayes Classifier. Rather than using traditional Naïve Bayes method, we have used logarithm based classifier that is more suitable for information retrieval tasks. We successfully evaluated the performance of our implementation using two other classification studies (Icsiboost-bigram and EM) on...
Defocusing is used in bright-field image processing in order to increase image contrast. Moreover, defocused images can be used to solve the transport of intensity equation (TIE) and obtain physical light phase. Recently, it was shown that the monogenic local features of an axial intensity derivative passed through a specific low-pass filter can be used to improve cell segmentation. In this paper,...
This paper presents a pairwise approach of finding shape correspondence for the construction of SSM, applied to distal femur bone. The correspondence was calculated by adapting a representative shape (template mesh) onto the training shapes in two steps. The first step was global registration which involved initial template mesh deformation using Laplacian Surface Deformation (LSD) method guided by...
This paper reports the preliminary development of a water-ski simulator for indoor training. Compared to existing training systems, the proposed simulator is capable of recreating a more realistic and immersive simulation experience, by providing both a proprioceptive and visual feedback to the practicing skier. In addition, it allows to practically test any desired skiing manoeuvre, since the ski...
Dictionary learning has been applied to various computer vision problems, such as image restoration, object classification and face recognition. In this work, we propose a tracking framework based on sparse representation and online discriminative dictionary learning. By associating dictionary items with label information, the learned dictionary is both reconstructive and discriminative, which better...
Recently, there has been a growing interest in developing Computer Aided Diagnostic (CAD) systems for improving the reliability and consistency of pathology test results. This paper describes a novel CAD system for the Anti-Nuclear Antibody (ANA) test via Indirect Immunofluorescence protocol on Human Epithelial Type 2 (HEp-2) cells. While prior works have primarily focused on classifying cell images...
In this paper, a semi autonomous robot design serves the purpose of automatic loading and unloading of blocks using Image processing and machine learning. The automation part includes automatically detecting the distance and loading/unloading of the load object. The robot undergoes semi unsupervised learning. Distance, is measured using single camera based on pixel area measurement. A G.U.I is present...
In this paper a new method for identifying best switching option in reconfiguration of Radial Distribution Systems (RDS) is presented. Feeder reconfiguration is defined as the technique to alter the structures in the distribution feeder by opening and closing the sectionalizing and tie switches. The reconfiguration includes selecting of set of sectional switches to be opened and tie switch to be closed...
The One Class Support Vector Machine (OC-SVM) classifier has been used in many applications. Its main advantage is to train the classifier using only patterns belonging to the target class distribution. The OC-SVM is effective when large samples are available for providing an accurate classification. However, in some applications, as in handwritten signature verification, available handwritten signatures...
We investigate discriminative features that are able to improve classification accuracy on visually similar classes. To this end, we build a deep feature learning network, which learns features with discriminative constraint in each single layer module, and learns multiple levels of features for hierarchical image representation. Specifically, the network encodes the discriminative information by...
Recent years have seen a great inclination towards Machine Learning classification and researchers are thinking in terms of achieving accuracy and correctness. Many studied have proved that an ensemble of classifiers outperform individual ones in terms of accuracy. Qamar et al. have developed a Similarity Learning Algorithm (SiLA) based on a combination of k nearest neighbor algorithm and Voted Perceptron...
Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme...
The ultimate goal of distance metric learning is to use discriminative information to keep data samples in the same class close, and those in different classes separate. Local distance metric methods can preserve discriminative information by considering neighborhood influence. We propose a discriminative distance metric approach by maximizing local pair wise constraints. Based on the local learning...
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