Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier
Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.

title: Image Computing: Fractional Spectra and Circular Moments via FrFT
abstract: In image computing, feature extraction plays a key part for image pattern classification. In this article we adopt discrete Fractional Fourier Transform (FrFT) for fractional feature extraction. Firstly, a criterion is proposed to determine the FrFT order for an image class so that it may be optimally discriminated from other classes in the FrFT domain, and the transformed features are called fractional spectra. Secondly, four types of fractional moments respectively called circular center, circular range, circular skewness, and circular kurtosis are defined and computed from the FrFT results of an image with different FrFT orders. The extracted image features are then classified with a previously proposed nonlinear classifier called Kernel-based Nonlinear Representor (KNR). And face recognition experiments are taken for illustrative examples.

title: A Discriminative Approach to Topic-Based Citation Recommendation
abstract: In this paper, we present a study of a novel problem, i.e. topic-based citation recommendation, which involves recommending papers to be referred to. Traditionally, this problem is usually treated as an engineering issue and dealt with using heuristics. This paper gives a formalization of topic-based citation recommendation and proposes a discriminative approach to this problem. Specifically, it proposes a two-layer Restricted Boltzmann Machine model, called RBM-CS, which can discover topic distributions of paper content and citation relationship simultaneously. Experimental results demonstrate that RBM-CS can significantly outperform baseline methods for citation recommendation.

title: A Novel Approach Using PCA and SVM for Face Detection
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

title: An Improved Fuzzy Image Enhancement Algorithm
abstract: An effective method for fuzzy image enhancement was presented by Russo, which was controlled by tuning of one parameter. The fixed parameter was used for the entire image resulting in over blurring or sharpening of image features in some parts of the image. On the basis of these, in this paper, we applied Russo's algorithm on different kinds of noise and proposed an efficient method to obtain the parameter values adaptively. Firstly, each pixel went through a phase of smoothing and then followed by a phase of combining smoothing and sharpening; Secondly, to effectively enhance image, each pixel local feature was adaptively assigned different parameter values by evaluating pixel local features through a fuzzy membership function; Finally, compared with Russopsilas method and other methods, experimental results indicated that the proposed method could achieve better performance than traditional methods for the enhancement of images corrupted with impulse and Gaussian noise.

title: Index Support for Visualizing Large, Distributed, High-Dimension Data Sets

title: MDDAS 2008 Workshop Organization
abstract: A combination capacitor and transistor structure is described wherein the capacitor is formed integrally with the emitter electrode of the transistor. The transistor is formed in a monolithic integrated circuit using generally known techniques and constitutes a vertically integrated PNP device. The emitter electrode of the transistor which comprises a P+ diffusion region is of a predetermined area which is large enough to form the bottom plate of the capacitor. The top plate of the capacitor is formed by growing a dielectric material over the diffused emitter region and then forming metallization thereover. The combination capacitor and transistor structure may be utilized in a bias network for biasing the output stage of an operational amplifier in a class AB mode. The capacitor formed in the combination structure may be utilized as the compensation capacitor in such operational amplifier which utilizes pole splitting techniques. The improvement provided by the invention reduces the surface area of the semiconductor die chip required to form the capacitor and transistor which facilitates greater device density on a particular die chip.