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Inquisitive pattern recognition

Inquisitive pattern recognition

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3rd international conference on Information Fusion, FUSION 2000

作者： Magnus, Amy L. Gustafson, Steven C. Intelligent Information AFRL/IFTD Rome Research Site Rome NY United States Department of Electrical and Computer Engineering Air Force Institute of Technology Wright Patterson AFB OH United States

ISBN: (纸本)2725700000

In nature, inquisitiveness is the drive to question, to seek a deeper understanding and to challenge assumptions. Within the discrete world of computers, inquisitive pattern recognition (IPR) is the constructive investigation and exploitation of conflict in information. data fusion is fertile proving-ground for inquisitive technologies. Multi-source, multi-modal data inherently contain conflicting information. As data fusion advances capabilities in situation assessment, strategies to identify and resolve conflict become important. IPR is a persistent, unsupervised learning capability whose concepts include falsification-similar to the supervised learning technique of cross-validation-and the classification of confusion in feature space. Coupled with knowledge base technologies, IPR enables a computer to acquire new experiences. © 2000 Int. Soc. Inf. Fusion.

关键词： data fusion

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Graph-based induction for general graph structured data and its application to chemical compound data 3rd

引用

3rd international conference on Discovery Science, DS 2000

作者： Matsuda, Takashi Horiuchi, Tadashi Motoda, Hiroshi Washio, Takashi I.S.I.R Osaka University 8-1 Mihogaoka IbarakiOsaka567-0047 Japan

ISBN: (纸本)9783540413523

Most of the relations are represented by a graph structure, e.g., chemical bonding, Web browsing record, DNA sequence, Inference pattern (program trace), to name a few. Thus, efficiently finding characteristic substructures in a graph will be a useful technique in many important KDD/ML applications. However, graph pattern matching is a hard problem. We propose a machine learning technique called Graph-Based Induction (GBI) that efficiently extracts typical patterns from graph data in an approximate manner by stepwise pair expansion (pair-wise chunking). It can handle general graph structured data, i.e., directed/undirected, colored/uncolored graphs with/without (self) loop and with colored/uncolored links. We show that its time complexity is almost linear with the size of graph. We, further, show that GBI can effectively be applied to the extraction of typical patterns from chemical compound data from which to generate classification rules, and that GBI also works as a feature construction component for other machine learning tools. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Artificial intelligence

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3rd international Workshop on Recent Advances in Intrusion Detection, RAID 2000

3rd International Workshop on Recent Advances in Intrusion D...

引用

3rd international Workshop on Recent Advances in Intrusion Detection, RAID 2000

ISBN: (纸本)9783540410850

The proceedings contain 14 papers. The special focus in this conference is on Advances in Intrusion Detection. The topics include: better logging through formality;a pattern matching based filter for audit reduction and fast detection of potential intrusions;transaction-based pseudonyms in audit data for privacy respecting intrusion detection;a data mining and CIDF based approach for detecting novel and distributed intrusions;using finite automata to mine execution data for intrusion detection;adaptive, model-based monitoring for cyber attack detection;a real-time intrusion detection system based on learning program behavior;intrusion detection using variable-length audit trail patterns;analysis and results of the 1999 DARPA off-line intrusion detection evaluation;using rule-based activity descriptions to evaluate intrusion-detection systems;a language to model a database for detection of attacks and target naming and service apoptosis.

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Joint IAPR international Workshops on Structural, Syntactic pattern recognition and Statistical Techniques in pattern recognition, SSPR 2000 and SPR 2000

Joint IAPR International Workshops on Structural, Syntactic ...

引用

international Association of pattern recognition, IAPR 2000 held jointly with 8th international Workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd international Workshop on Statistical Techniques in pattern recognition, SPR 2000

ISBN: (纸本)3540679464

The proceedings contain 92 papers. The special focus in this conference is on Pierre Devijver Lecture and Hybrid and Combined Methods. The topics include: Some notes on twenty one 21 nearest prototype classifiers;adaptive graphical pattern recognition beyond connectionist-based approaches;current trends in grammatical inference;classifier's complexity control while training multilayer perceptrons;a framework for classifier fusion;image pattern recognition based on examples;a hybrid system for the recognition of hand-written characters;improving statistical measures of feature subsets by conventional and evolutionary approaches;selection of classifiers based on multiple classifier behaviour;the adaptive subspace map for image description and image database retrieval;adaptative automatic target recognition with SVM boosting for outlier detection;a multiresolution causal colour texture model;writer identification;syntactic pattern recognition by error correcting analysis on tree automata;offline recognition of syntax-constrained cursive handwritten text;structural classification for retrospective conversion of documents;segmentation of date field on bank cheques;grammars and discourse theory to describe and recognize mechanical assemblies;computation of the N best parse trees for weighted and stochastic context-free grammars;partitional vs hierarchical clustering using a minimum grammar complexity approach;encoding nondeterministic finite-state tree automata in sigmoid recursive neural networks;a structural matching algorithm using generalized deterministic annealing;alignment and correspondence using singular value decomposition;fast candidate elimination using machine learning techniques and efficient alignment and correspondence using edit distance.

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Language learning with a neighbor system? 3rd

引用

3rd international conference on Discovery Science, DS 2000

作者： Mukouchi, Yasuhito Sato, Masako Department of Mathematics and Information Sciences College of Integrated Arts and Sciences Osaka Prefecture University SakaiOsaka599-8531 Japan

ISBN: (纸本)9783540413523

We consider inductive language learning from positive examples, some of which may be incorrect. In the present paper, the error or incorrectness we consider is the one described uniformly in terms of a distance over strings. Firstly, we introduce a notion of a recursively generable distance over strings, and define a k-neighbor closure of a language L as the collection of strings each of which is at most k distant from some string in L. Then we define a k-neighbor system as the collection of original languages and their j-neighbor closures with j ≤ k, and adopt it as a hypothesis space. In ordinary learning paradigm, a target language, whose examples are fed to an inference machine, is assumed to belong to a hypothesis space without any guarantee. In this paper, we allow an inference machine to infer a neighbor closure instead of the original language as an admissible approximation. We formalize such kind of inference, and give some sufficient conditions for a hypothesis space. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Artificial intelligence

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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...

引用

15th international conference on pattern recognition, ICPR 2000

作者： Valdés, Julio J. Molina, Luis C. Espinosa, Sergio Polytechnic University of Catalonia Department of Languages and Information Systems Campus Nord. Modul C6-201 Jordi Girona Salgado 1-3 Barcelona08034 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 differen t 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 multi-fractal dimension). Not so many quantization levels of fractal dimension variables are required in order to achieve high recognition performance. © 2000 IEEE.

关键词： Fractal dimension

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Multi-sensor 3D image fusion and interactive search

Multi-sensor 3D image fusion and interactive search

引用

3rd international conference on Information Fusion, FUSION 2000

作者： Ross, W.D. Waxman, A.M. Streilein, W.W. Aguiiar, M. Verly, J. Liu, F. Braun, M.I. Harmon, P. Rak, S. Massachusetts Institute of Technology Lincoln Laboratory Lexington MA 02420 United States MCIS Department Jacksonville State University Jacksonville AL 36265 United States

ISBN: (纸本)2725700000

We describe a system under development for the 3D fusion of multi-sensor surface surveillance imagery, including electro-optical (EO), IR, SAR, multispectral and hyperspectral sources. Our approach is founded on biologically-inspired image processing algorithms. We have developed an image processing architecture enabling the unified interactive visualization of fused multi-sensor site data which utilizes a color image fusion algorithm based on retinal and cortical processing of color. We have also developed interactive Web-based tools for training neural network search agents that are capable of automatically scanning site data for the fused multi-sensor signatures of targets and/or surface features of interest. Each search agent is an interactively trained instance of a neural network model of cortical pattern recognition called a fuzzy ARTMAP. The utilization of 3D site models is central to our approach because it enables the accurate multi-platform image registration that is necessary for color image fusion and the designation, learning and searching for multi-sensor fused pixel signatures. Interactive stereo 3D viewing and fly-through tools enable efficient and intuitive site exploration and analysis. Web-based remote visualization and search agent training and utilization tools facilitate rapid, distributed and collaborative site exploitation and dissemination of results. © 2000 Int. Soc. Inf. Fusion.

关键词： Image fusion

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3rd international Symposium on Intelligent data Analysis, IDA 1999

3rd International Symposium on Intelligent Data Analysis, ID...

引用

3rd international Symposium on Intelligent data Analysis, IDA 1999

ISBN: (纸本)3540663320

The proceedings contain 44 papers. The special focus in this conference is on learning and Visualization. The topics include: From theoretical learnability to statistical measures of the learnable;a methodology for designing accurate linguistic models for intelligent data analysis;mining clusters with association rules;evolutionary computation to search for strongly correlated variables in high-dimensional time-series;the biases of decision tree pruning strategies;feature selection as retrospective pruning in hierarchical clustering;discriminative power of input features in a fuzzy model;learning elements of representations for redescribing robot experiences;intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic;monitoring human information processing via intelligent data analysis of EEG recordings;knowledge-based visualization to support spatial data mining;navigating through large text collections;3D grand tour for multidimensional data and clusters;a decision tree algorithm for ordinal classification;discovering dynamics using bayesian clustering;integrating declarative knowledge in hierarchical clustering tasks;nonparametric linear discriminant analysis by recursive optimization with random initialization;supervised classification problems;temporal pattern generation using hidden markov model based unsupervised classification;exploiting similarity for supporting data analysis and problem solving;multiple prototype model for fuzzy clustering;a comparison of genetic programming variants for data classification;fuzzy clustering based on modified distance measures and building classes in object-based languages by automatic clustering.

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Problem of selecting optimal subset of fuzzy-valued features

Problem of selecting optimal subset of fuzzy-valued features

引用

1999 IEEE international conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics'

作者： Wang, X.Z. Tsang, E.C.C. Yeung, D.S. Hong Kong Polytechnic Univ Kowloon Hong Kong

Feature subset selection refers to a data mining enhancement technique which aims to reduce the number of features to be used. This reduction is expected to improve the performance of data mining algorithms to be used, in aspects of speed, accuracy and simplicity. Although there has been some work on feature subset selection, the research on the theoretically computational complexity of this problem and on the optimal selection of fuzzy-valued feature subsets has not been found. This paper focuses on a problem called Optimal Fuzzy-valued Feature Subset Selection (OFFSS) which is regarded as being important but difficult in machine learning and pattern recognition. The measure of the quality of a set of features is defined by the overall overlapping degree between two classes of examples and the size of feature subset. Main contributions of this paper are that: (1) the concept of fuzzy extension matrix is introduced, (2) the computational complexity of OFFSS is proved to be NP-hard, (3) a simple but powerful heuristic algorithm for OFFSS is given, and (4) the feasibility and simplicity of the proposed algorithm are demonstrated via applications of OFFSS to input selection of neuro-fuzzy systems and the fuzzy decision tree induction.

关键词： learning systems

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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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