Support vector machines (SVM) are learning algorithms derived from statistical learningtheory. the SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-c...
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the proceedings contain 198 papers. the topics discussed include: where is the intelligence in machine vision?;radial-basis-function networks: learning and applications;dimensionality reduction techniques for multivar...
ISBN:
(纸本)0780364007
the proceedings contain 198 papers. the topics discussed include: where is the intelligence in machine vision?;radial-basis-function networks: learning and applications;dimensionality reduction techniques for multivariate data classification, interactive visualization, and analysis-systematic feature selection vs. extraction;integrating community services- a common infrastructure proposal;communication-support for ongoing conversations in users' background knowledge;change in human behaviors based on affiliation needs - toward the design of a social guide agent system;consumer communication on the net-findings from consumer interviews;learning incremental syntactic structures with recursive neural networks;computational capabilities of linear recursive networks;a generalized regression neural network for logo recognition;and a proposal of fuzzy modeling with dimensionality reduction incorporating fuzzy inference method.
the proceedings contain 198 papers. the topics discussed include: where is the intelligence in machine vision?;radial-basis-function networks: learning and applications;dimensionality reduction techniques for multivar...
ISBN:
(纸本)0780364007
the proceedings contain 198 papers. the topics discussed include: where is the intelligence in machine vision?;radial-basis-function networks: learning and applications;dimensionality reduction techniques for multivariate data classification, interactive visualization, and analysis-systematic feature selection vs. extraction;integrating community services- a common infrastructure proposal;communication-support for ongoing conversations in users' background knowledge;change in human behaviors based on affiliation needs - toward the design of a social guide agent system;consumer communication on the net-findings from consumer interviews;learning incremental syntactic structures with recursive neural networks;computational capabilities of linear recursive networks;a generalized regression neural network for logo recognition;and a proposal of fuzzy modeling with dimensionality reduction incorporating fuzzy inference method.
In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some operating modes will usually be missing. In these circumstances, it is important to know which m...
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In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some of the operating modes are often missing. In these circumstances, it is important to know which ...
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ISBN:
(纸本)0769507506
In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some of the operating modes are often missing. In these circumstances, it is important to know which modes (subclasses) are the most valuable for successful machine diagnosis. It is also of interest to investigate the usefulness of noise injection to cover the missing operating modes in the data. In this paper, we study the importance of selecting different operating modes of a water-pump and using them for learning in both 2-class and 4-class problems. We show that the operating modes representing different running speeds are more valuable than those representing machine loads. We also demonstrate that the 2-nearest neighbours directed noise injection is useful when filing in missing operating modes in the data.
the goal of character recognition research is to simplify and automate the development of character recognition algorithms. We describe an approach based on applying preprocessing to data sets of Latin characters and ...
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the proceedings contain 23 papers. the special focus in this conference is on Grammatical Inference. the topics include: Results of the abbadingo one DFA learning competition and a new evidence-driven state merging al...
ISBN:
(纸本)3540647767
the proceedings contain 23 papers. the special focus in this conference is on Grammatical Inference. the topics include: Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm;learning k-variable pattern languages efficiently stochastically finite on average from positive data;meaning helps learning syntax;a polynomial time incremental algorithm for learning DFA;the data driven approach applied to the OSTIA algorithm;grammar model and grammar induction in the system NL PAGE;approximate learning of random subsequential transducers;learning stochastic finite automata from experts;learning deterministic finite automaton with a recurrent neural network;applying grammatical inference in learning a language model for oral dialogue;real language learning;a stochastic search approach to grammar induction;transducer-learning experiments on language understanding;locally threshold testable languages in strict sense;learning a subclass of linear languages from positive structural information;grammatical inference in document recognition;stochastic inference of regular tree languages;how considering incompatible state mergings may reduce the DFA induction search tree;learning regular grammars to model musical style;learning a subclass of context-free languages;using symbol clustering to improve probabilistic automaton inference and a performance evaluation of automatic survey classifiers;pattern discovery in biosequences.
this paper presents a prototypical digital library service. It integrates machinelearning tools and techniques in order to make effective, efficient and economically feasible the process of capturing the information ...
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this paper presents a prototypical digital library service. It integrates machinelearning tools and techniques in order to make effective, efficient and economically feasible the process of capturing the information that should be stored and indexed by content in the digital library. In fact, information capture is one of the main bottleneck when building a digital library, since it involves complex patternrecognition problems, such as document analysis, classification and understanding. Experimental results show that learning systems can solve effectively and efficiently all these problems.
Assisted document recognition systems have to integrate automatic recognition, manual edition and incremental learning in a single interactive environment. this paper raises the question of the organization of these t...
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Assisted document recognition systems have to integrate automatic recognition, manual edition and incremental learning in a single interactive environment. this paper raises the question of the organization of these three kinds of operations. When an analyzer has the ability to improve with use, there is a tradeoff between the benefits of enhancing the accuracy of automatic analysis, and the additional time spent in interacting for feedback communication. the global cost depends then on the sequence of processed entities, and on the relevance of the learning transactions. Notations are introduced to describe the evolution of a recognition session, and possible organization strategies are discussed. then a cost model is presented to allow the comparison between different organization schemes. We describe some concrete experiments of cost measures withthe ApOFIS font identification tool and the ScanWorX OCR;the first results show that a user-driven approach can potentially save substantial effort in the recognition process, in comparison withmachine-driven systems.
the architecture of a system for reading machine-printed documents in known predefined tabular-data layout styles is described. In these tables, textual data are presented in record lines made up of fixed-width fields...
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the architecture of a system for reading machine-printed documents in known predefined tabular-data layout styles is described. In these tables, textual data are presented in record lines made up of fixed-width fields. the system performs these steps: copes with multiple tables per page;identifies records within tables;segments records into fields;and recognizes characters within fields, constrained by field-specific contextual knowledge. Obstacles to good performance on tables include small print, tight line-spacing, poor-quality text, and line-art or background patterns that touch the text.
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