UID:
almahu_9948621575402882
Umfang:
XII, 296 p.
,
online resource.
Ausgabe:
1st ed. 2004.
ISBN:
9783540301950
Serie:
Lecture Notes in Artificial Intelligence ; 3264
Anmerkung:
Invited Papers -- Learning and Mathematics -- Learning Finite-State Models for Machine Translation -- The Omphalos Context-Free Grammar Learning Competition -- Regular Papers -- Mutually Compatible and Incompatible Merges for the Search of the Smallest Consistent DFA -- Faster Gradient Descent Training of Hidden Markov Models, Using Individual Learning Rate Adaptation -- Learning Mild Context-Sensitiveness: Toward Understanding Children's Language Learning -- Learnability of Pregroup Grammars -- A Markovian Approach to the Induction of Regular String Distributions -- Learning Node Selecting Tree Transducer from Completely Annotated Examples -- Identifying Clusters from Positive Data -- Introducing Domain and Typing Bias in Automata Inference -- Analogical Equations in Sequences: Definition and Resolution -- Representing Languages by Learnable Rewriting Systems -- A Divide-and-Conquer Approach to Acquire Syntactic Categories -- Grammatical Inference Using Suffix Trees -- Learning Stochastic Finite Automata -- Navigation Pattern Discovery Using Grammatical Inference -- A Corpus-Driven Context-Free Approximation of Head-Driven Phrase Structure Grammar -- Partial Learning Using Link Grammars Data -- eg-GRIDS: Context-Free Grammatical Inference from Positive Examples Using Genetic Search -- The Boisdale Algorithm - An Induction Method for a Subclass of Unification Grammar from Positive Data -- Learning Stochastic Deterministic Regular Languages -- Polynomial Time Identification of Strict Deterministic Restricted One-Counter Automata in Some Class from Positive Data -- Poster Papers -- Learning Syntax from Function Words -- Running FCRPNI in Efficient Time for Piecewise and Right Piecewise Testable Languages -- Extracting Minimum Length Document Type Definitions Is NP-Hard -- Learning Distinguishable Linear Grammars from Positive Data -- Extending Incremental Learning of Context Free Grammars in Synapse -- Identifying Left-Right Deterministic Linear Languages -- Efficient Learning of k-Reversible Context-Free Grammars from Positive Structural Examples -- An Analysis of Examples and a Search Space for PAC Learning of Simple Deterministic Languages with Membership Queries.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783662189726
Weitere Ausg.:
Printed edition: ISBN 9783540234104
Sprache:
Englisch
URL:
https://doi.org/10.1007/b101520
