Computational Learning Theory
Springer Berlin (Verlag)
978-3-540-59119-1 (ISBN)
The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.
The discovery of algorithmic probability: A guide for the programming of true creativity.- A desicion-theoretic generalization of on-line learning and an application to boosting.- Online learning versus offline learning.- Learning distributions by their density levels - A paradigm for learning without a teacher.- Tight worst-case loss bounds for predicting with expert advice.- On-line maximum likelihood prediction with respect to general loss functions.- The power of procrastination in inductive inference: How it depends on used ordinal notations.- Learnability of Kolmogorov-easy circuit expressions via queries.- Trading monotonicity demands versus mind changes.- Learning recursive functions from approximations.- On the intrinsic complexity of learning.- The structure of intrinsic complexity of learning.- Kolmogorov numberings and minimal identification.- Stochastic complexity in learning.- Function learning from interpolation (extended abstract).- Approximation and learning of convex superpositions.- Minimum description length estimators under the optimal coding scheme.- MDL learning of unions of simple pattern languages from positive examples.- A note on the use of probabilities by mechanical learners.- Characterizing rational versus exponential learning curves.- Is pocket algorithm optimal?.- Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks.- A space-bounded learning algorithm for axis-parallel rectangles.- Learning decision lists and trees with equivalence-queries.- Bounding VC-dimension for neural networks: Progress and prospects.- Average case analysis of a learning algorithm for ?-DNF expressions.- Learning by extended statistical queries and its relation to PAC learning.- Typed pattern languages and theirlearnability.- Learning behaviors of automata from shortest counterexamples.- Learning of regular expressions by pattern matching.- The query complexity of learning some subclasses of context-free grammars.
| Erscheint lt. Verlag | 23.2.1995 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XVIII, 422 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 572 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | algorithm • Algorithm analysis and problem complexity • Algorithmisches Lernen • algorithms • Algorithmus • Computational Learning • evolutionary algorithm • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Künstliche Intelligenz • learning • Learning theory • Lernalgorithmen • Logic • machine learning • Maschinelles Lernen • Neural networks • Neuronale Netze • Probabilistic Complexity • programming • robot • Robotics • Wahrscheinlichkeitsrechnung • Wahrscheinlichkeitstheoretische Komplexität |
| ISBN-10 | 3-540-59119-2 / 3540591192 |
| ISBN-13 | 978-3-540-59119-1 / 9783540591191 |
| Zustand | Neuware |
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