14770227
Data smoothing regularization, multi-sets-learning, and problem solving strategies
First, we briefly introduce the basic idea of data smoothing regularization, which was firstly proposed by Xu [Brain-like computing and intelligent information systems (1997) 241] for parameter learning in a way similar to Tikhonov regularization but with an easy solution to the difficulty of determining an appropriate hyper-parameter. Also, the roles of this regularization are demonstrated on Gaussian-mixture via smoothed versions of the EM algorithm, the BYY model selection criterion, adaptive harmony algorithm as well as its related Rival penalized competitive learning. Second, these studies are extended to a mixture of reconstruction errors of Gaussian types, which provides a new probabilistic formulation for the multi-sets learning approach [Proc. IEEE ICNN94 I (1994) 315] that learns multiple objects in typical geometrical structures such as points, lines, hyperplanes, circles, ellipses, and templates of given shapes. Finally, insights are provided on three problem solving strategies, namely the competition-penalty adaptation based learning, the global evidence accumulation based selection, and the guess-test based decision, with a general problem solving paradigm suggested.
443631
Learned parametric mixture based ICA algorithm
Abstract The learned parametric mixture method is presented for a canonical cost function based ICA model on linear mixture, with several new findings. First, its adaptive algorithm is further refined into a simple concise form. Second, the separation ability of this method is shown to be qualitatively superior to its original model with prefixed nonlinearity. Third, a heuristic way is suggested for selecting the number of densities in a learned parametric mixture. Finally, experiments have been conducted to show the success of this method on the sources that can either be sub-Gaussian or super-Gaussian, as well as a combination of both the types.
7432960
Bayesian Self-Organization
Recent work by Becker and Hinton (Becker and Hinton, 1992) shows a promising mechanism, based on maximizing mutual information assuming spatial coherence, by which a system can self-organize itself to learn visual abilities such as binocular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theories of visual perception and to other organization principles for early vision (Atick and Redlich, 1990). Methods for implementation using variants of stochastic learning are described and, for the special case of linear filtering, we derive an analytic expression for the output.
8090511
Bayesian Kullback Ying-Yang dependence reduction theory
Bayesian Kullback Ying—Yang dependence reduction system and theory is presented. Via stochastic approximation, implementable algorithms and criteria are given for parameter learning and model selection, respectively. Three typical architectures are further studied on several special cases. The forward one is a general information theoretic dependence reduction model that maps an observation x into a representation y of k independent components, with k detectable by criteria. For the special cases of invertible map xPy, a general adaptive algorithm is obtained, which not only is applicable to nonlinear or post-nonlinear mixtures, but also provides an adaptive EM algorithm that implements the previously proposed learned parametric mixture method for independent component analysis (ICA) on linear mixtures. The backward architecture provides a maximum likelihood independent factor model for modeling observations from unknown number of independent factors via a linear or nonlinear system under noisy situations. For the special cases of linear or post-nonlinear mixture under Gaussian noise, the simplified adaptive algorithm and the criterion for detecting k are given, with an approximately optimal linear mapping xPy suggested. Moreover, if the independent factors are assumed to be standard Gaussians, we are further led to the conventional factor analysis, but with a new adaptive algorithm for its estimation and a criterion for deciding the number of factors. The bi-directional architecture combines the advantages of backward and forward ones. A mean field approximation is presented, with a simplified adaptive parameter learning algorithm and an approximate k-selection criterion. Moreover, its special cases lead to the existing least mean square error reconstruction learning and the one hidden layer deterministic Helmholtz machine, with new findings. Also, a specific degenerate case of bi-directional architecture results in a non-invertible but adaptively implementable forward on to mapping for ICA. Experiments on binary sources are demonstrated with successes. ( 1998 Elsevier Science B.V. All rights reserved.
6364266
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
The Bayesian Ying-Yang (BYY) harmony learning acts as a general statistical learning framework, featured by not only new regularization techniques for parameter learning but also a new mechanism that implements model selection either automatically during parameter learning or via a new class of model selection criteria used after parameter learning. In this paper, further advances on BYY harmony learning by considering modular inner representations are presented in three parts. One consists of results on unsupervisedmixture models, ranging from Gaussian mixture based Mean Square Error (MSE) clustering, elliptic clustering, subspace clustering to NonGaussian mixture based clustering not only with each cluster represented via either Bernoulli-Gaussian mixtures or independent real factor models, but also with independent component analysis implicitly made on each cluster. The second consists of results on supervised mixture-of-experts (ME) models, including Gaussian ME, Radial Basis Function nets, and Kernel regressions. The third consists of two strategies for extending the above structural mixtures into self-organized topological maps. All these advances are introduced with details on three issues, namely, (a) adaptive learning algorithms, especially elliptic, subspace, and structural rival penalized competitive learning algorithms, with model selection made automatically during learning; (b) model selection criteria for being used after parameter learning, and (c) how these learning algorithms and criteria are obtained from typical special cases of BYY harmony learning.
205119948
On radial basis function nets and kernel regression: Statistical consistency, convergence rates, and receptive field size
Abstract Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) are established. By using existing theoretical results obtained for KRE as tools, we obtain a number of interesting theoretical results for RBF nets. Upper bounds are presented for convergence rates of the approximation error with respect to the number of hidden units. The existence of a consistent estimator for RBF nets is proven constructively. Upper bounds are also provided for the pointwise and L2 convergence rates of the best consistent estimator for RBF nets as the numbers of both the samples and the hidden units tend to infinity. Moreover, the problem of selecting the appropriate size of the receptive field of the radial basis function is theoretically investigated and the way this selection is influenced by various factors is elaborated. In addition, some results are also given for the convergence of the empirical error obtained by the least squares estimator for RBF nets.
2996848
Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data
MOTIVATION
DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers.


RESULTS
We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis.


CONTACT
leixu@jhu.edu.
32570723
Parallel algorithm for mining fuzzy association rules
The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.
8691766
Media Streaming via TFRC: An Analytical Study of the Impact of TFRC on User-Perceived Media Quality
TCP-Friendly Rate Control (TFRC) is being adopted in Internet standards for congestion control of various streaming media applications. In this paper, we consider the transmission of pre-recorded media from a server to a client by using TFRC, and analytically study the impact of TFRC on user-perceived media quality, which is roughly measured by calculating the rebuffering probability. A rebuffering probability is defined to be the probability that the total duration of all rebuffering events experienced by a user is longer than a certain threshold. Two approaches are presented to help an application determine an appropriate initial buffering delay and media playback rate in order to achieve a certain rebuffering probability under a given network condition. First, we derive a closed- form expression to approximate the average TFRC sending rate, which could be used as the maximum allowed playback rate of a media stream. Second, we develop a queueing model for a TFRC client buffer with the traffic described by a Markov-Renewal- Modulated Deterministic Process (MRMDP), and present an iterative method to calculate the rebuffering probability.
34666787
Applying Agent into Web Testing and Evolution
Automatic Web testing and evolution is an effective method to ensure the quality of Web applications. In this paper, we apply Agent into this area, i.e. firstly, we explain the necessity and feasibility, and also give the general description of our method; then, we apply Agent into the Web performance testing, Web regression testing and Web usability evolution; conclusion remarks are given at last.
7919733
Discovering direct and indirect matches for schema elements
Automating schema matching is challenging. Previous approaches to automating schema matching focus on computing direct element matches between two schemas. Schemas, however rarely match directly. Thus, to complete the task of schema matching, we must also compute indirect element matches. In this paper we present a framework for generating direct as well as many indirect element matches between a source schema and a target schema. Recognizing expected data values associated with schema elements and applying schema-structure heuristics are the key ideas to computing indirect matches. Experiments we have conducted over several real-world application domains show encouraging results, yielding over 90% precision and recall for both direct and indirect element matches.