In nature, inquisitiveness is the drive to question, to seek a deeper understanding and to challenge assumptions. Within the discrete world of computers, inquisitive patternrecognition (IPR) is the constructive inves...
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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 substru...
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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 a...
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 datamining 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.
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;adap...
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 patternrecognition beyond connectionist-based approaches;current trends in grammatical inference;classifier's complexity control while training multilayer perceptrons;a framework for classifier fusion;image patternrecognition 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 patternrecognition 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 machinelearning techniques and efficient alignment and correspondence using edit distance.
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 s...
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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...
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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 biolo...
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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 de...
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 datamining;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.
Feature subset selection refers to a datamining enhancement technique which aims to reduce the number of features to be used. This reduction is expected to improve the performance of datamining algorithms to be used...
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Feature subset selection refers to a datamining enhancement technique which aims to reduce the number of features to be used. This reduction is expected to improve the performance of datamining 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 machinelearning and patternrecognition. 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.
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 w...
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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, patternrecognition, and machinelearning, 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 machinelearning, 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 datamining applications. We discuss important design decisions and explain the interdependencies with the properties of data. We then intro
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