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Gabor-Atom networks based radar target identification

Gabor-Atom networks based radar target identification

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international conference on Signal Processing Proceedings (ICSP)

作者： Yu Shi Xian-Da Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

A Gabor-Atom network (GAN) approach for application in radar target recognition is proposed. the Gabor atoms selected by a multilayer feedforward neural network extract discriminant features among different classes of radar target returns. the self-learning mechanism is used not only for the network but for the feature parameters. Results on the classification of microwave anechoic chamber data of three different scaled airplane models are presented.

关键词： Multi-layer neural network Gallium nitride Radar applications Target recognition Neural networks Feedforward neural networks data mining Feature extraction Anechoic chambers Airplanes

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Behavior analysis of fractal features for texture description in digital images: an experimental study

Behavior analysis of fractal features for texture descriptio...

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international conference on pattern recognition

作者： J.J. Valdes L.C. Molina S. Espinosa Department of Languages and Information systems Polytechnic university of Catalonia Barcelona Spain

ISBN: (纸本)0769507506

Texture-based recognition for image segmentation and classification is very important in many domains and different numerical features coming from a variety of approaches have been proposed. Texture segmentation using six features based on the fractal dimension has been used elsewhere. this paper, studies properties of these features from the point of view of dimensionality reduction, mutual relation, differential relevance, discrete quantization, and classification ability. In an experimental framework, a set of statistical, soft computing, data mining and machine learning methods were used on a set of different textures (multidimensional scaling, rough sets, factor analysis, cluster analysis and inductive classification). It was found that fractal features effectively have texture recognition ability. Some of these are very relevant (the fractal dimension of smoothed versions of the original image and the multifractal dimension). Not so many quantisation levels of fractal dimension variables are required in order to achieve high recognition performance.

关键词： Image analysis Image texture analysis Fractals Digital images Image segmentation Quantization data mining Information analysis Information systems Image recognition

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Clustering very large databases using EM mixture models

Clustering very large databases using EM mixture models

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international conference on pattern recognition

作者： P.S. Bradley U.M. Fayyad C.A. Reina Microsoft Research Limited China

ISBN: (纸本)0769507506

Clustering very large databases is a challenge for traditional pattern recognition algorithms, e.g. the expectation-maximization (EM) algorithm for fitting mixture models, because of high memory and iteration requirements. Over large databases, the cost of the numerous scans required to converge and large memory requirement of the algorithm becomes prohibitive. We present a decomposition of the EM algorithm requiring a small amount of memory by limiting iterations to small data subsets. the scalable EM approach requires at most one database scan and is based on identifying regions of the data that are discardable, regions that are compressible, and regions that must be maintained in memory. data resolution is preserved to the extent possible based upon the size of the memory buffer and fit of the current model to the data. Computational tests demonstrate that the scalable scheme outperforms similarly constrained EM approaches.

关键词： Clustering algorithms Read-write memory pattern recognition data mining Visual databases Maximum likelihood estimation Probability density function machine learning algorithms Distributed databases Costs

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Approximate clustering in association rules

Approximate clustering in association rules

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： L.J. Mazlack Computer Science Nippon Steel Information and Communication Systems Inc. Cincinnati OH USA

data mining holds the promise of extracting unsuspected information from very large databases. One difficulty is that discovery techniques are often drawn from methods in which the amount of work increases geometrically with data quantity. Consequentially, the use of these methods is problematic in very large databases. Categorically based association rules are a linearly complex data mining methodology. Unfortunately, rules formed from categorical data often generate many fine grained rules. the concern is how fine grained rules might be aggregated and the role that non-categorical data might have. It appears that soft computing techniques may be useful.

关键词： Association rules data mining data analysis machine learning Computer science Humans Clustering algorithms Spatial databases Telephony pattern recognition

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learning to extract temporal signal patterns from temporal signal sequence

Learning to extract temporal signal patterns from temporal s...

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international conference on pattern recognition

作者： Pengyu Hong T.S. Huang Beckman Institute University of Illinois Urbana-Champaign Urbana IL USA

ISBN: (纸本)0769507506

We propose an approach that extracts patterns from a temporal signal sequence without prior knowledge about the lengths, positions and the number of the patterns. Previous research (Hong et al., 1999) proposes a scheme for extracting recurrent patterns from a noise free signal without temporal warping. To handle noise and nonlinear temporal warping, a threshold finite state machine (TFSM) is proposed to perform spatial-temporal data modeling. the TFSM is first roughly initialized. A variance of segmental K-means is used to train the TFSM. the training results give us both the patterns embedding in the signal sequence and the trained TFSM that can be used to represent and detect the patterns.

关键词： Hidden Markov models Multiple signal classification data mining Automata Humans Clustering algorithms Magnetic heads Speech Acoustic signal detection Unsupervised learning

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Tutorial 3. Clustering Techniques for Large data Sets - From the Past to the Future 5

Tutorial 3. Clustering Techniques for Large Data Sets - From...

引用

5th ACM SIGKDD international conference on Knowledge Discovery and data mining, KDD 1999

作者： Keim, Daniel A. Hinneburg, Alexander University of Munich Germany University of Halle Germany

ISBN: (纸本)1581131712

Because of the fast technological progress, the amount of information which is stored in databases is rapidly increasing. In addition, new applications require the storage and retrieval of complex multimedia objects which are often represented by high-dimensional feature vectors. Finding the valuable information hidden in those databases is a difficult task. Cluster analysis is one of the basic techniques which is often applied in analyzing large data sets. Originating from the area of statistics, most cluster analysis algorithms have originally been developed for relatively small data sets. In the recent years, the clustering algorithms have been extended to efficiently work on large data sets, and some of them even allow the clustering of high-dimensional feature vectors. Many such methods use some kind of an index structure for an efficient retrieval of the required data;other approaches are based on preprocessing for a more efficient clustering. the main goal of the tutorial is to provide an overview of the state-of-the-Art in cluster discovery methods for large databases, covering well-known clustering methods from related fields such as statistics, pattern recognition, and machine learning, as well as database techniques which allow them to work efficiently on large databases. the target audience of the tutorial are researchers and practitioners from statistics, databases, and machine learning, who are interested in the state-of-the art of cluster discovery methods and their applications to large databases. the tutorial especially addresses people from academia who are interested in developing new cluster discovery algorithms, and people from industry who want to apply cluster discovery methods in analyzing large databases. the tutorial is structured as follows: First, we give a brief motivation for clustering from modern data mining applications. We discuss important design decisions and explain the interdependencies with the properties of data. We then intro

关键词： Cluster analysis

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Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data 5

Performance evaluation of a new hybrid modeling technique fo...

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5th international conference on Document Analysis and recognition, ICDAR 1999

作者： Brakensiek, A. Kosmala, A. Willett, D. Wang, W. Rigoll, G. Dept. of Computer Science Faculty of Electrical Engineering Gerhard-Mercator-University Duisburg Duisburg47057 Germany

ISBN: (纸本)0769503187

the paper deals with the performance evaluation of a novel hybrid approach to large vocabulary cursive handwriting recognition and contains various innovations. 1) It presents the investigation of a new hybrid approach to handwriting recognition, consisting of hidden Markov models (HMMs) and neural networks trained with a special information theory based training criterion. this approach has only been recently introduced successfully to online handwriting recognition and is now investigated for the first time for offline recognition. 2) the hybrid approach is extensively compared to traditional HMM modeling techniques and the superior performance of the new hybrid approach is demonstrated. 3) the data for the comparison has been obtained from a database containing online handwritten data which has been converted to offline data. therefore, a multiple evaluation has been carried out, incorporating the comparison of different modeling techniques and the additional comparison of each technique for online and offline recognition, using a unique database. the results confirm that online recognition leads to better recognition results due to the dynamic information of the data, but also show that it is possible to obtain recognition rates for offline recognition that are close to the results obtained for online recognition. Furthermore, it can be shown that for both online and offline recognition, the new hybrid approach clearly outperforms the competing traditional HMM techniques. It is also shown that the new hybrid approach yields superior results for the offline recognition of machine printed multifont characters. © 1999 IEEE.

关键词： Hidden Markov models

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Tutorial 4. mining unstructured data 5

Tutorial 4. Mining unstructured data

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5th ACM SIGKDD international conference on Knowledge Discovery and data mining, KDD 1999

作者： Feldman, Ronen Bar-Ilan University Israel

ISBN: (纸本)1581131712

the information age has made it easy to store large amounts of data. the proliferation of documents available on the Web, on corporate intranets, on news wires, and elsewhere is overwhelming. However, while the amount of data available to us is constantly increasing, our ability to absorb and process this information remains constant. Search engines only exacerbate the problem by making more and more documents available in a matter of a few key strokes. Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from data mining, machine learning, NLP, IR and knowledge management. Text mining involves the preprocessing of document collections (text categorization, term extraction), the storage of the intermediate representations, the techniques to analyze these intermediate representations (distribution analysis, clustering, trend analysis, association rules etc) and visualization of the results. In this tutorial we will present the general theory of Text mining and will demonstrate several systems that use these principles to enable interactive exploration of large textual collections. We will present a general architecture for text mining and will outline the algorithms and data structures behind the systems. Special emphasis will be given to efficient algorithms for very large document collections, tools for visualizing such document collections, the use of intelligent agents to perform text mining on the internet, and the use of information extraction to better capture the major themes of the documents. the tutorial will cover the state of the art in this rapidly growing area of research. Several real world applications of text mining will be presented. © ACM 1999.

关键词： Digital storage

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High performance data mining and knowledge discovery

High performance data mining and knowledge discovery

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5th international conference on Parallel Processing, Euro-Par 1999

作者： Skillicorn, David Talia, Domenico

ISBN: (纸本)3540664432

Many, perhaps most, organizations use computers when they interact with their customers. As a result, and almost by accident, many organizations have accumulated huge amounts of data about such interactions. Over the past five to ten years, they have increasingly tried to use this data for commercial advantage. this process began by accumulating transaction data into data warehouses, where it could be made available for decision support and retrospective analysis. the effeectiveness of such analysis largely depends on the ability of individuals to induce queries that will reveal key facts about the organization and its customers. Increasingly, both the volume of data and its complexity have taken the problem beyond the ability of any individual to analyze. data mining is the automated analysis of large volumes of data, looking for the relationships and knowledge that are implicit in large volumes of data and are ‘interesting’ in the sense of impacting an organization’s practice. Research and development work in the area of knowledge discovery and data mining concerns the study and definition of techniques, methods, and tools for the extraction of novel, useful, and implicit patterns from data. It builds on machine learning, database technology, and statistics, but is distinguished by problems of scale: the data involved is so large that most applications tend to use conceptually straightforward, but carefully optimized, algorithms.

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Using decision tree induction for discovering holes in data 5th

Using decision tree induction for discovering holes in data

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5th Pacific Rim Intemational conference on Artificial Intelligence, PRICAI 1998

作者： Liu, Bing Wang, Ke Mun, Lai-Fun Qi, Xin-Zhi School of Computing National University of Singapore Lower Kent Ridge Road 119260 Singapore

ISBN: (纸本)354065271X

Existing research in machine learning and data mining has been focused on finding rules or regularities among the data cases. Recently, it was shown that those associations that are missing in data may also be interesting. these missing associations are the holes or empty regions. the existing algorithm for discovering holes has a number of shortcomings. It requires each hole to contain no data point, which is too restrictive for many real-life applications. It also has a very high complexity, and produces a huge number of holes. Additionally, the algorithm only works in a continuous space, and does not allow any discrete/nominal attribute. these drawbacks limit its applications. In this paper, we propose a novel approach to overcome these shortcomings. this approach transforms the holes-discovery problem into a supervised learning task, and then uses the decision tree induction technique for discovering holes in data. © Springer-Verlag Berlin Heidelberg 1998.

关键词： data mining

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