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In order to solve multi-class classification problem in real world, we improved TSVM in this paper. We combined LSTSVM with partial binary tree to improve classification efficiency. Binary tree hierarchy can solved the inseparable regional issues in OVO-SVM and OVA-SVM classification. Experimental results show that it improved the classification accuracy. It also has better speed-up ratio than the...
Physiological sensors are widely used in order to infer the mental effort of a subject during performing different tasks. Desktop or mobile applications like educational games can gain from such information in order to fine tune the difficulty or the type of a given assignment. Discussions can be found on the advantages and disadvantages of different sensor types (like EEG, ECG, pupillometry, GSR...
The identification and classification of underwater noise sources is of utmost importance in modern underwater acoustic signal processing systems. Dynamic and complicated oceanic environment makes underwater target classification a challenging task. An underwater acoustic target classification system identifies the acoustics targets from a mixture of acoustic events through their characteristic acoustic...
A novel version of multi-class classification method based on fruit fly optimization algorithm (FOA) and relevance vector machine (RVM) is proposed. The one-against-one-against-rest (OAOAR) classification model based on the traditional one-against-one (OAO) and one-against-rest (OAR) algorithm is aimed at combining the advantages of them and translates the multi-class classification problem into multiple...
Most of current clustering methods are designed for general purpose other than a specific color pixel classification use. Color Line model representation emerged as the ultimate method for clustering pixels using RGB color components. However, this method is strongly sensitive to the adjustment of input parameters, which cannot conform to the frequent change of image structures and compositions. In...
Sparse Representation based Classifier (SRC) is widely used in a variety of pattern recognition and machine learning tasks. The kernelized version of the classifier (Kernel SRC) attempts to remove the SRC limitations by a nonlinear mapping of the data through kernel functions. However, the performance of such a method strongly relies on the choice of the kernel function. In this paper, we firstly...
Computer vision is widely used at present. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of complexity and overlap. In this paper, a fruit recognition algorithm based on convolution neural network(CNN) is proposed. At first the image regions are extracted using selective search algorithm, then the regions have been selected by means of an entropy of...
Support vector machine (SVM) plays an important part in fault diagnosis of chemical plant, and intelligent optimization algorithms are used to optimize the SVM parameters, including the penalty parameter C and parameter g of different kernel function, to improve performance of its faults classification. To assess SVM faults classification capability based on diverse optimization algorithms and various...
This paper applies fuzzy clustering algorithm to recognize the transformer winding's pressed state based on transformer's vibration signal. We propose a new semi-supervised fuzzy kernel clustering algorithm (SFKC) based on some modifications for the fuzzy clustering methods. The first modification is that the new algorithm uses prior knowledge to guide the clustering process. Second, it uses kernel...
In order to make transformer potential fault diagnose effectively, Support Vector Machine (SVM) is introduced as an effective algorithm. Firstly, the above SVM algorithm is formed by four common kernel functions: linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function, Secondly, Differential Evolution Algorithm (DEA) based on new fitness function is introduced...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
In modern networks, there exist different applications which generate various different types of network traffic. In order to improve the performance of network management, it is important to identify and classify the internet traffic. The machine learning (ML) technique based on per-flow statistics has been widely used in traffic classification. Different from traditional classification methods,...
With the development of hyperspectral remote sensing information processing, hyperspectral image classification becomes a hot topic. The algorithm of kernel sparse representation classification based on spatial-spectral graph regularization and sparsity concentration index (SSGSCI-KSRC) gains a good result. Due to the big scale of hyperspectral image data, time-critical requirement in the practical...
Separating the foreground objects from the complex background in a static image is one of the research hot spots in computer vision. Due to lack of motion information, most of the current approaches only explore local object cues in the segment-level which easily suffer from not only the view and illumination changes, but also deformation and occlusion. This paper proposed a new multi-class object...
Visual target tracking is one of fundamental research of computer vision field and play an important role in the surveillance application, but it is also one of the difficulties due to the instability of the tracking scene. In this paper, we analyze the major drawbacks of the original Kernelized Correlation Filter (KCF) tracker which causes tracking failure when target experience complicated scenarios...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
A novel method called boundary distance is proposed for pre-extracting support vectors. It first calculates the distance between the sample and the other class sample. According to sort distance, the less distance samples, and nearest neighbor samples in the other class, are used as boundary samples. As the boundary samples include most support vectors, it greatly declines the training time without...
In order to reduce the computational complexity of kernel machines and improve their performance in multi-label classification, we develop a systematic two step batch approach for constructing and training a new multiclass kernel machine (MKM). The proposed paradigm prunes the kernels, and uses Newton's method to improve the kernel parameters. In each iteration, output weights are found using orthogonal...
In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information...
An automatic change detection method based on conditional random field (CRF) is presented for high resolution remote sensing images in this paper. Marginalized denoising autoencoder is used to generate the difference image. The clustering results of Fuzzy C-means are applied to initialize the unary potentials of CRF. A scaled squared Euclidean distance between neighboring pixels in the observed images...
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