The Experts below are selected from a list of 1737 Experts worldwide ranked by ideXlab platform
Stephen Kwek - One of the best experts on this subject based on the ideXlab platform.
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on learning unions of pattern languages and tree patterns
Algorithmic Learning Theory, 1999Co-Authors: Sally A Goldman, Stephen KwekAbstract:We present efficient on-line algorithms for learning unions of a constant number of tree patterns, unions of a constant number of one-variable pattern languages, and unions of a constant number of pattern languages with fixed length substitutions. By fixed length substitutions we mean that each occurence of variable xi must be substituted by Terminal strings of fixed length l(xi). We prove that if an arbitrary unions of pattern languages with fixed length substitutions can be learned efficiently then DNFs are efficiently learnable in the mistake bound model. Since we use a reduction to Winnow, our algorithms are robust against attribute noise. Furthermore, they can be modified to handle concept drift. Also, our approach is quite general and may be applicable to learning other pattern related classes. For example, we could learn a more general pattern language class in which a penalty (i.e. weight) is assigned to each violation of the rule that a Terminal Symbol cannot be changed or that a pair of variable Symbols, of the same variable, must be substituted by the same Terminal string. An instance is positive iff the penalty incurred for violating these rules is below a given tolerable threshold.
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ALT - On Learning Unions of Pattern Languages and Tree Patterns
Lecture Notes in Computer Science, 1999Co-Authors: Sally A Goldman, Stephen KwekAbstract:We present efficient on-line algorithms for learning unions of a constant number of tree patterns, unions of a constant number of one-variable pattern languages, and unions of a constant number of pattern languages with fixed length substitutions. By fixed length substitutions we mean that each occurence of variable xi must be substituted by Terminal strings of fixed length l(xi). We prove that if an arbitrary unions of pattern languages with fixed length substitutions can be learned efficiently then DNFs are efficiently learnable in the mistake bound model. Since we use a reduction to Winnow, our algorithms are robust against attribute noise. Furthermore, they can be modified to handle concept drift. Also, our approach is quite general and may be applicable to learning other pattern related classes. For example, we could learn a more general pattern language class in which a penalty (i.e. weight) is assigned to each violation of the rule that a Terminal Symbol cannot be changed or that a pair of variable Symbols, of the same variable, must be substituted by the same Terminal string. An instance is positive iff the penalty incurred for violating these rules is below a given tolerable threshold.
Sally A Goldman - One of the best experts on this subject based on the ideXlab platform.
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on learning unions of pattern languages and tree patterns
Algorithmic Learning Theory, 1999Co-Authors: Sally A Goldman, Stephen KwekAbstract:We present efficient on-line algorithms for learning unions of a constant number of tree patterns, unions of a constant number of one-variable pattern languages, and unions of a constant number of pattern languages with fixed length substitutions. By fixed length substitutions we mean that each occurence of variable xi must be substituted by Terminal strings of fixed length l(xi). We prove that if an arbitrary unions of pattern languages with fixed length substitutions can be learned efficiently then DNFs are efficiently learnable in the mistake bound model. Since we use a reduction to Winnow, our algorithms are robust against attribute noise. Furthermore, they can be modified to handle concept drift. Also, our approach is quite general and may be applicable to learning other pattern related classes. For example, we could learn a more general pattern language class in which a penalty (i.e. weight) is assigned to each violation of the rule that a Terminal Symbol cannot be changed or that a pair of variable Symbols, of the same variable, must be substituted by the same Terminal string. An instance is positive iff the penalty incurred for violating these rules is below a given tolerable threshold.
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ALT - On Learning Unions of Pattern Languages and Tree Patterns
Lecture Notes in Computer Science, 1999Co-Authors: Sally A Goldman, Stephen KwekAbstract:We present efficient on-line algorithms for learning unions of a constant number of tree patterns, unions of a constant number of one-variable pattern languages, and unions of a constant number of pattern languages with fixed length substitutions. By fixed length substitutions we mean that each occurence of variable xi must be substituted by Terminal strings of fixed length l(xi). We prove that if an arbitrary unions of pattern languages with fixed length substitutions can be learned efficiently then DNFs are efficiently learnable in the mistake bound model. Since we use a reduction to Winnow, our algorithms are robust against attribute noise. Furthermore, they can be modified to handle concept drift. Also, our approach is quite general and may be applicable to learning other pattern related classes. For example, we could learn a more general pattern language class in which a penalty (i.e. weight) is assigned to each violation of the rule that a Terminal Symbol cannot be changed or that a pair of variable Symbols, of the same variable, must be substituted by the same Terminal string. An instance is positive iff the penalty incurred for violating these rules is below a given tolerable threshold.
Masayuki Takeda - One of the best experts on this subject based on the ideXlab platform.
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Linear-Time text compression by longest-first substitution
Algorithms, 2009Co-Authors: Ryosuke Nakamura, Shunsuke Inenaga, Hideo Bannai, Takashi Funamoto, Masayuki Takeda, Ayumi ShinoharaAbstract:We consider grammar-based text compression with longest first substitution (LFS), where non-overlapping occurrences of a longest repeating factor of the input text are replaced by a new non-Terminal Symbol. We present the first linear-time algorithm for LFS. Our algorithm employs a new data structure called sparse lazy suffix trees. We also deal with a more sophisticated version of LFS, called LFS2, that allows better compression. The first linear-time algorithm for LFS2 is also presented.
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DCC - Simple Linear-Time Off-Line Text Compression by Longest-First Substitution
2007 Data Compression Conference (DCC'07), 2007Co-Authors: Ryosuke Nakamura, Shunsuke Inenaga, Hideo Bannai, Masayuki TakedaAbstract:We consider grammar based text compression with longest-first substitution, where non-overlapping occurrences of a longest repeating substring of the input text are replaced by a new non-Terminal Symbol. We present a new text compression algorithm by simplifying the algorithm presented in S. Inenaga et al., (2003). We give a new formulation of the correctness proof introducing the sparse lazy suffix tree data structure. We also present another type of longest-first substitution strategy that allows better compression. We show results of preliminary experiments comparing grammar sizes of the two versions of the longest-first strategy and the most frequent strategy
Ryosuke Nakamura - One of the best experts on this subject based on the ideXlab platform.
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Linear-Time text compression by longest-first substitution
Algorithms, 2009Co-Authors: Ryosuke Nakamura, Shunsuke Inenaga, Hideo Bannai, Takashi Funamoto, Masayuki Takeda, Ayumi ShinoharaAbstract:We consider grammar-based text compression with longest first substitution (LFS), where non-overlapping occurrences of a longest repeating factor of the input text are replaced by a new non-Terminal Symbol. We present the first linear-time algorithm for LFS. Our algorithm employs a new data structure called sparse lazy suffix trees. We also deal with a more sophisticated version of LFS, called LFS2, that allows better compression. The first linear-time algorithm for LFS2 is also presented.
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DCC - Simple Linear-Time Off-Line Text Compression by Longest-First Substitution
2007 Data Compression Conference (DCC'07), 2007Co-Authors: Ryosuke Nakamura, Shunsuke Inenaga, Hideo Bannai, Masayuki TakedaAbstract:We consider grammar based text compression with longest-first substitution, where non-overlapping occurrences of a longest repeating substring of the input text are replaced by a new non-Terminal Symbol. We present a new text compression algorithm by simplifying the algorithm presented in S. Inenaga et al., (2003). We give a new formulation of the correctness proof introducing the sparse lazy suffix tree data structure. We also present another type of longest-first substitution strategy that allows better compression. We show results of preliminary experiments comparing grammar sizes of the two versions of the longest-first strategy and the most frequent strategy
Hideo Bannai - One of the best experts on this subject based on the ideXlab platform.
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Linear-Time text compression by longest-first substitution
Algorithms, 2009Co-Authors: Ryosuke Nakamura, Shunsuke Inenaga, Hideo Bannai, Takashi Funamoto, Masayuki Takeda, Ayumi ShinoharaAbstract:We consider grammar-based text compression with longest first substitution (LFS), where non-overlapping occurrences of a longest repeating factor of the input text are replaced by a new non-Terminal Symbol. We present the first linear-time algorithm for LFS. Our algorithm employs a new data structure called sparse lazy suffix trees. We also deal with a more sophisticated version of LFS, called LFS2, that allows better compression. The first linear-time algorithm for LFS2 is also presented.
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DCC - Simple Linear-Time Off-Line Text Compression by Longest-First Substitution
2007 Data Compression Conference (DCC'07), 2007Co-Authors: Ryosuke Nakamura, Shunsuke Inenaga, Hideo Bannai, Masayuki TakedaAbstract:We consider grammar based text compression with longest-first substitution, where non-overlapping occurrences of a longest repeating substring of the input text are replaced by a new non-Terminal Symbol. We present a new text compression algorithm by simplifying the algorithm presented in S. Inenaga et al., (2003). We give a new formulation of the correctness proof introducing the sparse lazy suffix tree data structure. We also present another type of longest-first substitution strategy that allows better compression. We show results of preliminary experiments comparing grammar sizes of the two versions of the longest-first strategy and the most frequent strategy
