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2003 IEEE Aerospace Conference

作者： Lynch Jr., R.S. Willett, P.K. Signal Processing Branch Code 82 12 Naval Undersea Warfare Center Newport RI 02841 United States Department of Electrical and Computer Engineering University of Connecticut Storrs CT 06269 United States

ISBN: (纸本)078037651X

In this paper, the Mean-Field Bayesian Data Reduction Algorithm has been extended to adaptively train on data containing missing values. In the basic data model for this algorithm each feature vector of a given class contains a class-labeling feature. That is, while training on a data set the Mean-Field Bayesian Data Reduction Algorithm labels the unlabeled adapted data, while simultaneously determining those features that provide best classification performance. This has the benefit of improving performance by reducing the data to mitigate the effects of the curse of dimensionality. In the results shown, the Mean-Field Bayesian Data Reduction Algorithm is used to classify real-life data sets found at the UCI Repository. The emphasis in the results is on demonstrating performance for situations in which increasing numbers of training data are lacking any class labels. This is useful in illustrating the effect that removing class labels has on the overall probability of error in correct classification, as compared to the certain labeled case. In general, it is shown that the methods developed here are can significantly improve performance when the proportion of uncertain-labeled training data is very large. © 2003 IEEE.

关键词： Classification (of information)

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： Hong Zhao N. Ansari Y.Q. Shi Center for Communications and Signal Processing Research Department of Electrical and Computer Engineering New Jersey Institute of Technology University Heights Newark NJ USA

Recent studies of high quality; high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, least mean kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the least mean square (LMS) algorithm.

关键词： Traffic control Telecommunication traffic Predictive models Least squares approximation Aggregates Character generation Internet Mathematical model Resource management Higher order statistics

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Support vector machine classifications for microarray expression data set 5

Support vector machine classifications for microarray expres...

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5th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

作者： Zhang, Junying Lee, R. Wang, Y.J. National Key Lab. of Radar Signal Processing Xidian University Xi'an China Department of Electrical Engineering and Computer Science Catholic University of America WashingtonDC United States Lombardi Cancer Center Georgetown university WashingtonDC United States

ISBN: (纸本)0769519571

Gene selection, cancer classification and functional gene classification are three main concerns and interests by biologists for cancer detection, cancer classification, and understanding the functions of genes from the molecular level of tissues, where the large number of genes and relatively small number of experiments in gene expression data generate a great challenge. After a brief introduction of support vector machine(SVM) for classification, this paper presents recent SVM approaches for gene selection, cancer classification and functional gene classification followed by analysis on the advantages and limitations of SVM on these applications. © 2003 IEEE.

关键词： Support vector machines

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Use of Bayesian data reduction for the fusion of legacy classifiers

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Information Fusion 2003年第1期4卷 23-34页

作者： Robert S. Lynch Peter K. Willett Signal Processing Branch Code 8212 Naval Undersea Warfare Center Newport RI USA Department of Electrical and Computer Engineering University of Connecticut Storrs CT USA

For important classification tasks there may already be extant an arsenal of classification tools, these representing previous attempts and best efforts at a solution. Many times these are useful classifiers; and although the fact that all base their decisions on the same observations implies that their decisions are strongly dependent in a way that is difficult to model, there is often some benefit from fusing them to a better corporate decision. One can consider this fusion as of building a meta-classifier, based on data vectors whose elements are the individual legacy classifier (LC) decisions. The Bayesian data reduction algorithm imposes a uniform prior probability mass function on discrete symbol probabilities. It was developed previously, and in this paper is applied to the preceding decision-fusion problem, with favorable comparison to a number of other expert-mixing approaches. Parameters varied include the number of relevant LCs (some may have been poorly designed, and ought to be discounted/discarded automatically), the numbers of training data and classes, and the dependence between LCs––a fusion approach should reject redundant decisions.

关键词： Feature selection Multinomial Dirichlet Neural network Linear classifier (weighted voting) Majority voting

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Time-varying AR modeling and subspace projection for FM jammer suppression in DS/SS-CDMA systems

Time-varying AR modeling and subspace projection for FM jamm...

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Asilomar Conference on signals, Systems & computers

作者： Lichuan Liu Hongya Ge Center for Communication and Signal Processing Research Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)0780381041

In this paper, we propose a new technique to effectively suppress nonstationary jammer in DS/SS communication systems. This technique combines a new scheme of instantaneous frequency (IF) estimation with the projection based interference suppression. In order to capture jammer subspace a time-varying autoregressive (TV-AR) modeling is used to estimate the IFs of the nonstationary jammers. The jammer subspace is constructed from the models governed by the estimated time-varying IFs. The estimated jamming interference is removed from the received data by subspace projection, resulting in less distortion to the desired signal. The performance of this approach is analyzed and compared with approaches using time-varying notch filtering and subspace projection method.

关键词： Time varying systems Jamming Frequency modulation signal processing Interference suppression Frequency estimation Distortion Filtering Convergence Filters

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Blind equalization of communication sequences based on optimization of cumulant criteria

Blind equalization of communication sequences based on optim...

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IEEE Conference on Wireless communications and Networking

作者： Hsiao-Chun Wu Dongxin Xu Communications and Signal Processing Laboratory Department of Electrical and Computer Engineering Louisiana State University Baton Rouge LA USA BBN Technologies GTE Cambridge MA USA

Blind equalization draws a lot of attention. Several statistical objective functions such as kurtosis and constant modulus were based on the noise-free model and hence their ISI cancellers were sensitive to noise. In this paper, a unifying adaptive blind equalization method is proposed, which can be robust to noise than the current cumulant-based adaptive methods. Our new objective functions can be applied for wired or wireless i.i.d. communication symbols with noise. The simulation shows that our new method outperforms the kurtosis-based method when the background noise exists.

关键词： Blind equalizers Probability density function signal processing Finite impulse response filter Laboratories Delay Linear approximation Gaussian noise Sufficient conditions Taylor series

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Bayesian classification results using data containing missing class labels

Bayesian classification results using data containing missin...

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IEEE Conference on Aerospace

作者： R.S. Lynch P.K. Willett Signal Processing Branch Naval Undersea Warfare Center Newport RI USA Department of Electrical and Computer Engineering University of Connecticut CT USA

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Non-coherent estimator-correlators for unresolved multipath Ricean channels

Non-coherent estimator-correlators for unresolved multipath ...

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IEEE Conference on Vehicular Technology (VTC)

作者： F. Danilo-Lemoine H. Leib Department of Syst. and Computer Engineering Broadband Communications & Wireless Systems Centre Carleton University Ottawa ONT Canada Department of Electrical and Computer Engineering McGill University Telecommunications and Signal Processing Laboratory Montreal QUE Canada

This paper considers detection techniques for unresolved Ricean/Rayleigh fading multipath channels. It is well known that the optimal receiver has an estimator-correlator form, with a minimum mean-square error (MMSE) estimate. With known multipath delays and unknown specular phases, the Ricean non-coherent multipath channel is a case where the received noiseless signal process is non-Gaussian, and the MMSE estimator is non-linear in the observation. This work presents novel, explicit expressions for such a MMSE estimator. It is shown that the MMSE estimate has the same multipath format as the noiseless received signal, with the unknown parameters replaced by corresponding estimates. The MMSE estimator scales down contributions from specular multipath components with poor phase estimates, reducing their effect on the detection process. The estimator includes a multipath decorrelation operation which is essential to avoid error floors over unresolved multipath channels. Based on matched filter bounds, it is shown that little degradation gains are obtained by employing suboptimal linear estimator-correlators, provided that they include the decorrelation operation.

关键词： Multipath channels Phase estimation Delay estimation Decorrelation Rayleigh channels Phase noise signal processing Phase detection Matched filters Degradation

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Identifying underlying factors in breast cancer using independent component analysis

Identifying underlying factors in breast cancer using indepe...

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IEEE Workshop on Neural Networks for signal processing

作者： J.A. Berger S. Hautaniemi H. Edgren O. Monni S.K. Mitra O. Yli-Harja J. Astola Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Tampere International Center for Signal Processing Tampere University of Technology Tampere FINLAND VTT Medical Biotechnology Turku FINLAND Biomedicum Biochip Center Helsinki Univ. Hospital Helsinki FINLAND

Independent component analysis is a well-known tool for extracting underlying mechanisms from an observed set of parallel data. Identifying such components in breast cancer cell lines, for both copy number and gene expression, is proposed here with the goal of identifying mechanisms that affect the evolution of breast cancer in humans. This paper illustrates how to utilize independent component analysis on cell line data for achieving this goal. After the components were estimated for the well-studied chromosome 17, and then over the entire genome for a set of 14 different breast cancer cell lines, ontological analysis was performed in order to determine common gene functions that are present in each of the independent components.

关键词： Breast cancer Independent component analysis Data mining Gene expression Humans Biological cells Genomics Bioinformatics Cells (biology) Ontologies

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Support vector machine classifications for microarray expression data set

Support vector machine classifications for microarray expres...

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International Conference on Computational Intelligence and Multimedia Applications

作者： Junying Zhang R. Lee Y.J. Wang Department of Electrical Engineering & Computer Science Catholic University of America Washington D.C. DC USA National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Lombardi Cancer Center George town University Washington D.C. DC USA

ISBN: (纸本)0769519571

关键词： Support vector machines Support vector machine classification Kernel Gene expression signal processing algorithms Cancer detection Cloning Radar signal processing computer science Biology

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