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ON THE DESIGN OF A TREE CLASSIFIER AND ITS APPLICATON TO SPEECH recognition

International Journal of pattern recognition and Artificial Intelligence 1991年第5期5卷 677-692页

作者： CHORKIN CHAN JUN BAO Department of Computer Science University of Hong Kong Hong Kong China Institute of Pattern Recognition Shanghai Jiao-tong University Shanghai China

A new algorithm for constructing entropy reduction based decision tree classifier in two steps for large reference-class sets is proposed. The d-dimensional feature space is first mapped onto a line thus allowing a dynamic choice of features and then the resultant linear space is partitioned into two sections while minimizing the average system entropy. Classes of each section, again considered as a collection of references classes in a d-dimensional feature space, can be further split in a similar manner should the collection still be considered excessively large, thus forming a binary decision tree of nodes with overlapping members. The advantage of using such a classifier is that the need to match a test feature vector against all the references exhaustively is avoided. We demonstrate in this paper that discrete syllable recognition with dynamic programming equipped with such a classifier can reduce the recognition time by a factor of 40 to 100. The recognition speed is one third to one half of that using hidden Markov models (HMM), while the recognition rate is somewhat higher. The theory is considerably simpler than that of HMM but the decision tree can occupy a lot of memory space.

关键词： Large reference set Decision tree Tree classifier Classifier Entropy reduction Speech recognition Discrete syllable recognition

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A THREE-LAYER ADAPTIVE NETWORK FOR pattern DENSITY ESTIMATION AND CLASSIFICATION

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International Journal of Neural Systems 1991年第3期2卷 211-220页

作者： Jian-xiong Wu Chorkin Chan Department of Computer Science University of Hong Kong Hong Kong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University P.R.C

Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer representing a Gaussian basis function (covariance matrix equal to where I is a unit matrix) to estimate various probability densities and serve as a Bayes classifier. The width of the basis function may be the same for all neurons in this layer or it may vary from one neuron to another. This paper investigates the effectiveness of the network for both cases and presents a localized learning algorithm to adjust the network parameters. The network was trained with artificial data derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. The performance of the network as a pattern density estimator was measured in terms of the relative difference between the target probability density function (p.d.f.) which generates the training and testing data and the network output representing the estimation. Samples from two mixtures corresponding to two classes were used to test the network capability as a classifier by comparing its error rate against that of a Bayes classifier. Both one- and two-dimensional cases were explored. The successfulness of the network depended on how well the target p.d.f.’s were represented by the training samples, the number of hidden neurons employed in the network and how thoroughly the network was trained. It was also found that allowing each basis function to have an independent width had a predominant effect on the network performance.

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NEURAL NETS FOR DUAL SUBSPACE pattern recognition METHOD

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International Journal of Neural Systems 1991年第3期2卷 169-184页

作者： Lei Xu Adam Krzyzak Erkki Oja Centre for Pattern Recognition & Machine Intelligence Dept. of Computer Science Concordia University 1455 deMaisonneuve Blvd. West Montreal PQ Canada H3G 1M8 Canada On leave from Dept. of Mathematics Peking University Beijing P.R. China. During Feb. 1989-May 1990 he was with Lappeeranta University of Technology. Presently he is with Harvard University Division of Applied Sciences. Lappeenranta University of Technology Department of Information Technology Box 20 53851 Lappeenranta Finland

A new modification of the subspace pattern recognition method, called the dual subspace pattern recognition (DSPR) method, is proposed, and neural network models combining both constrained Hebbian and anti-Hebbian learning rules are developed for implementing the DSPR method. An experimental comparison is made by using our model and a three-layer forward net with backpropagation learning. The results illustrate that our model can outperform the backpropagation model in suitable applications.

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ROLE OF SIMULATION IN RAPID PROTOTYPING FOR CONCEPT DEVELOPMENT

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 204-211页

作者： KING, JF BARTON, DE J. Fred King:is the manager of the Advanced Technology Department for Unisys in Reston Virginia. He earned his Ph.D. in mathematics from the University of Houston in 1977. He has been principal investigator of research projects in knowledge engineering pattern recognition and heuristic problem-solving. Efforts include the development of a multi-temporal multispectral classifier for identifying graincrops using LANDSAT satellite imagery data for NASA. Also as a member of the research team for a NCI study with Baylor College of Medicine and NASA he helped develop techniques for detection of carcinoma using multispectral microphotometer scans of lung tissue. He established and became technical director of the AI Laboratory for Ford Aerospace where he developed expert scheduling modeling and knowledge acquisition systems for NASA. Since joining Unisys in 1985 he has led the development of object-oriented programming environments blackboard architectures data fusion techniques using neural networks and intelligent data base systems. Douglas E. Barton:is manager of Logistics Information Systems for Unisys in Reston Virginia. He earned his B.A. degree in computer science from the College of William and Mary in 1978 and did postgraduate work in London as a Drapers Company scholar. Since joining Unisys in 1981 his work has concentrated on program management and software engineering of large scale data base management systems and design and implementation of knowledge-based systems in planning and logistics. As chairman of the Logistics Data Subcommittee of the National Security Industrial Association (NSIA) he led an industry initiative which examined concepts in knowledge-based systems in military logistics. His responsibilities also include evaluation development and tailoring of software engineering standards and procedures for data base and knowledge-based systems. He is currently program manager of the Navigation Information Management System which provides support to the Fleet Ballistic Missile Progr

A valuable technique during concept development is rapid prototyping of software for key design components. This approach is particularly useful when the optimum design approach is not readily apparent or several known alternatives need to be rapidly evaluated. A problem inherent in rapid prototyping is the lack of a "target system" with which to interface. Some alternatives are to develop test driver libraries, integrate the prototype with an existing working simulator, or build one for the specific problem. This paper presents a unique approach to concept development using rapid prototyping for concept development and scenario-based simulation for concept verification. The rapid prototyping environment, derived from artificial intelligence technology, is based on a blackboard architecture. The rapid prototype simulation capability is provided through an object-oriented modeling environment. It is shown how both simulation and blackboard technologies are used collectively to rapidly gain insight into a tenacious problem. A specific example will be discussed where this approach was used to evolve the logic of a mission controller for an autonomous underwater vehicle.

关键词： Military Engineering

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Nonlinear shape restoration by transformation models

Nonlinear shape restoration by transformation models

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International Conference on pattern recognition

作者： Y.Y. Tang C.Y. Suen Center for Pattern Recognition and Machine Intelligence Department of Computer Science Concordia University Montreal QUE Canada

An entropy-reduced transformation (ERT) approach to nonlinear shape restoration has been developed. Nonlinear shape distortions are formulated using nonlinear shape transformations derived from the finite-element theory. Several algorithms which perform the nonlinear shape transformations are given. The inverse nonlinear shape transformation algorithms are described. Some application experiments are described, and results are given.< >

关键词： Shape Nonlinear distortion pattern recognition Uncertainty Image restoration Finite element methods Cameras Entropy Machine intelligence computer science

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Specification of expert systems

Specification of expert systems

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： A. Batarekh A.D. Preece A. Bennett P. Grogono Centre for Pattern Recognition and Machine Intelligence Computer Science Department Concordia University Montreal QUE Canada

The authors focus on the problems of specification of an expert system namely, what needs to be specified, what can be specified and how. Two distinct major roles for a software specification are identified: as a contract between parties involved in system development and as a blueprint for the design and implementation of the system. It is shown that these purposes require quite different specifications. The role of specification as a contract is taken by the problem specification, which essentially describes what system is to be built. The blueprint specification is complementary, and describes how the system is to be built, including a description of the knowledge to be used and a description of how to represent and reason with that knowledge.< >

关键词： Expert systems Contracts pattern recognition Machine intelligence computer science Software systems Software quality Software algorithms System testing Electric breakdown

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SHAPE TRANSFORMATION MODELS AND THEIR APPLICATIONS IN pattern recognition

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International Journal of pattern recognition and Artificial Intelligence 1990年第1期4卷 65-94页

作者： Z.C. LI Y.Y. TANG T.D. BUI C.Y. SUEN Centre de recherche informatique de Montréal Inc. 1550 Maisonneuve Blvd. West Bureau 901 Montreal Quebec H3G 1N2 Canada Department of Computer Science Concordia University and Centre for Pattern Recognition and Machine Intelligence 1455 de Maisonneuve Blvd. West Montreal Quebec H3G 1M8 Canada

This paper presents linear and bilinear shape transformations including basic transformations, analyzes their geometric properties, and provides computer algorithms. The shape transformations can be used to simplify the recognition of Roman letters, Chinese characters and other pictorial patterns by normalizing their shapes to the standard forms. Important theoretical analyses have been performed to illustrate that the linear and bilinear transformations are applicable to computer recognition of digitized patterns. A number of pictorial examples have been computed to confirm the analyses and conclusions made.

关键词： Shape transformation Geometric transformation Linear transformation Bilinear transformation Angle distortion Distortion computer algorithm Geometric property Error analysis Character recognition Shape recognition One-to-one transformation Finite element method Numerical analysis

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CAD-based computer vision: from CAD models to relational graphs

CAD-based computer vision: from CAD models to relational gra...

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IEEE International Conference on Systems, Man and Cybernetics

作者： P.J. Flynn A.K. Jain Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to object a relational graph representation of the object which can be stored in a database of models of object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3-D models are discussed. In addition to the process of translation from one data format to another, the interference engine extracts higher-level information from the CAD model and stores it explicitly in the new data structure. The higher-level features will allow the search space explored during the object recognition stage to be pruned early.< >

关键词： computer vision Solid modeling Object recognition Engines Space exploration Industrial relations Relational databases Spatial databases Interference Data mining

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Preface [Special issue intro.]

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IEEE Transactions on pattern Analysis and Machine Intelligence 1979年第2期PAMI-1卷 125-126页

作者： Y. T. Chien Theodosios Pavlidis Guest Editor Professor and Head of the Department of Electrical Engineering and Computer Science. Guest Editor member of the Association for Computing Machinery and Sigma Xi member of the editorial committee of the IEEE TRANSACTIONS OF PATTERN ANALYSIS AND MACHINE INTELLIGENCE Associate Editor of the Bulletin of Mathematical Biology Computer Graphics and Image Processing and Pattern Recognition.

This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on pattern recognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop was sponsored by the Technical Committee on Machine Intelligence and pattern Analysis. Inevitably, the contributors to the Workshop determined, to a large degree, the tone and complexion of this Special Issue. For this reason, a brief account of the Workshop Proceedings, though now history, is given. About half of the papers presented at the Workshop were also submitted for the Special Issue, a total of 37. Those of high quality were far more than the number that could be accommodated within the available number of pages. We decided to choose three topics where the interaction between the methodologies of PR and Al was most prevelant: analysis of images, analysis of speech, and certain general algorithms. All the selected papers present either theoretical, or experimental results, or both. We felt that such results clearly demonstrate the progress achieved and can be seen as very impressive if measured against the difficult problem of emulating functions associated with human intelligence by machines. It is true that they often fall short from some of the earlier ambitious goals, but the time is probably ripe to reexamine such goals in view of the accumulated experience. The following is a brief scanning of the contents of this issue, especially as related to the integration and/or interaction of PR and Al methodologies.

关键词： Artificial intelligence pattern recognition Image analysis Speech analysis Conferences labeling Machine intelligence Algorithm design and analysis Image edge detection computer Society

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Machine Translation 1

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丛书名： Communications in computer and Information science

1000年

作者： Jiajun Chen Jiajun Zhang

ISBN: (数字)9789811330834

ISBN: (纸本)9789811330827

This book constitutes the refereed proceedings of the 14th China Workshop on Machine Translation, CWMT 2018, held in Wuyishan, China, in October 2018.;The 9 papers presented in this volume were carefully reviewed and selected from 17 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.

关键词： Natural Language Processing (NLP)

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