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Although data mining techniques are made tremendous progress, "knowledge-poor" is still a large gap of the current data mining systems. Few researches notice the fact that useful knowledge not only is the final results of an intelligent classification, clustering or prediction algorithm, but also runs through the whole process of data mining in which much potential useful information is...
The performance of a kernel-based method is usually sensitive to a choice of the values of the hyper parameters of a kernel function. In this paper, we present a novel framework of using wavelet kernels in the kernel principal component analysis (KPCA) in order to better explain the nonlinear relationships among original multivariate data. We propose to introduce dilation and translation factors into...
We present a technique for automatic diagnosis of malignant melanoma based exclusively on local pattern analysis. The technique relies on local binary patterns in small sections in the image, and automatically selects the relevant texture features from those that discriminate best between benign and malignant skin lesions. The classification is performed using support vector machines, and the feature...
Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and transductive support vector machine (TSVM) has been presented-RSTSVM-MIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions as instances, in order to transform every...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
Common visual codebook generation methods used in a Bag of Visual words model, e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual code words. However, most popular visual descriptors are histograms of image measurements. It has been shown that the Histogram Intersection Kernel (HIK) is more effective than the Euclidean distance in supervised learning...
Support vector clustering (SVC) is an appealing approach that can detect cluster boundaries. In spite of its popularization in applications, it sees the critical bottleneck in cluster labeling. This paper presents a novel support vector clustering algorithm (NSVC) to go a further step in clustering labeling. NSVC consists of three phases: extract data representatives (DRs); cluster DRs; label non-DR...
Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is an important problem in computer aided detection. To improve the performance of detection, we propose a bagging-based twin support vector machine (B-TWSVM) to detect MCs. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed...
This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying...
A method of the recognition of electricity equipments operation state (EEOS) is put up based on support vector machine (SVM). First Chinese character or number operation state images of electricity equipments are segmented with C-mean clustering. Then, feature vector of operation state image of electricity equipments is extracted using K-L transform. At last, classification method of SVM for state...
Clustered microcalcification is an important signal for breast cancer in the early stages. In this paper, we propose a multiple kernel SVM with group features (GF-SVM) to tackle problems associated with heterogeneous features of clustered microcalcification and normal breast tissues in suspicious regions. Specifically, different types of features such as being gradient, geometric and textural are...
This paper proposes a novel multi-class cluster support vector machine, which borrows ideas of nonparallel hyperplanes from generalized eigenvalue support vector machines. For a k-class classification problem, it trains k nonparallel hyperplanes respectively, and each one lies as close as possible to self-class while apart from the rest classes as far as possible. Then, the label of a new sample is...
To overcome the vast computation of standard SVM, a novel multi-reduced SVM method for speaker recognition is proposed in this paper. The proposed method consists of three parts. Firstly the entropy-based feature selection approach is exploited to reduce the dimension of the input vectors by extracting the important feature attributes, in which the performance of the clustering is improved. Secondly...
Facial expression recognition is an active research area that finds a potential application in human emotion analysis. This work presents an efficient approach of facial expression features clustering based on Support Vector Clustering (SVC). Common approaches to facial expression features clustering are designed considering two main parts: (1) features extraction, and (2) features clustering. In...
This paper presents a novel approach that recognizing heart rhythm with the combination of adaptive Hermite decomposition and support vector machines (SVM) classification. The novelty lies in two aspects. In the first aspect, for the goal of feature extraction, the orthogonal transformation based on Hermite basis functions is proposed to characterize the morphological features of ECG data. In the...
In this paper, we the look at the spectral properties of features extracted from segmented ECG signals containing Normal (N) and premature ventricular beats (V) prior to apply classification methods for reliable PVC detection. In a first stage, feature extraction based on signal basic analysis which computes not only intervals and amplitudes on each beat, but also description of wave morphology was...
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