Machine Learning: ECML'97
Springer Berlin (Verlag)
978-3-540-62858-3 (ISBN)
Uncertain learning agents.- Constructing and sharing perceptual distinctions.- On prediction by data compression.- Induction of feature terms with INDIE.- Exploiting qualitative knowledge to enhance skill acquisition.- Integrated learning and planning based on truncating temporal differences.- ?-subsumption for structural matching.- Classification by Voting Feature Intervals.- Constructing intermediate concepts by decomposition of real functions.- Conditions for Occam's razor applicability and noise elimination.- Learning different types of new attributes by combining the neural network and iterative attribute construction.- Metrics on terms and clauses.- Learning when negative examples abound.- A model for generalization based on confirmatory induction.- Learning Linear Constraints in Inductive Logic Programming.- Finite-Element methods with local triangulation refinement for continuous reinforcement learning problems.- Inductive Genetic Programming with Decision Trees.- Parallel anddistributed search for structure in multivariate time series.- Compression-based pruning of decision lists.- Probabilistic Incremental Program Evolution: Stochastic search through program space.- NeuroLinear: A system for extracting oblique decision rules from neural networks.- Inducing and using decision rules in the GRG knowledge discovery system.- Learning and exploitation do not conflict under minimax optimality.- Model combination in the multiple-data-batches scenario.- Search-based class discretization.- Natural ideal operators in Inductive Logic Programming.- A case study in loyalty and satisfaction research.- Ibots learn genuine team solutions.- Global data analysis and the fragmentation problem in decision tree induction.- Case-based learning: Beyond classification of feature vectors.- Empirical learning of Natural Language Processing tasks.- Human-Agent Interaction and Machine Learning.- Learning in dynamically changing domains: Theory revision and context dependence issues.
| Erscheint lt. Verlag | 9.4.1997 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XIV, 366 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 493 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Algorithm analysis and problem complexity • Algorithmic Learning • Algorithmisches Lernen • classification • Decision Making • Entscheidungstheorie • genetic programming • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Inductive Logic Programming • Induktives logisches Programmieren • learning • learning agents • Lernende Agenten • Logic • machine learning • Maschinelles Lernen • programming • Reinforcement Learning |
| ISBN-10 | 3-540-62858-4 / 3540628584 |
| ISBN-13 | 978-3-540-62858-3 / 9783540628583 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
