The following pages link to Machine Learning (Q65106):
Displaying 50 items.
- A polynomial approach to the constructive induction of structural knowledge (Q1326583) (← links)
- Flattening and saturation: Two representation changes for generalization (Q1326585) (← links)
- Explicit representation of concept negation (Q1326586) (← links)
- Algorithms and lower bounds for on-line learning of geometrical concepts (Q1329679) (← links)
- Discrete sequence prediction and its applications (Q1329680) (← links)
- Bias in information-based measures in decision tree induction (Q1332696) (← links)
- Associative reinforcement learning: A generate and test algorithm (Q1332697) (← links)
- Associative reinforcement learning: Functions in \(k\)-DNF (Q1332698) (← links)
- Toward an ideal trainer (Q1332699) (← links)
- Trading accuracy for simplicity in decision trees (Q1332700) (← links)
- Toward efficient agnostic learning (Q1342730) (← links)
- A theory for memory-based learning (Q1342731) (← links)
- The learnability of description logics with equality constraints (Q1342732) (← links)
- On-line learning of rectangles and unions of rectangles (Q1342733) (← links)
- Asynchronous stochastic approximation and Q-learning (Q1345139) (← links)
- Complexity-based induction (Q1345141) (← links)
- An upper bound on the loss from approximate optimal-value functions (Q1345144) (← links)
- Generalizing version spaces (Q1345145) (← links)
- Multistrategy learning. 2nd special issue. 3rd international workshop - MSL '96, Harpers Ferry, WV, USA, May 23--25, 1996 (Q1366528) (← links)
- A multistrategy approach to relational knowledge discovery in databases (Q1366530) (← links)
- Integrating multiple learning strategies in first order logics (Q1366531) (← links)
- Characteristic sets for polynomial grammatical inference (Q1366803) (← links)
- A Bayesian/information theoretic model of learning to learn via multiple task sampling (Q1369061) (← links)
- CHILD: A first step towards continual learning (Q1369063) (← links)
- Inductive transfer. 2nd special issue (Q1369064) (← links)
- Selective sampling using the query by committee algorithm (Q1369065) (← links)
- Malicious omissions and errors in answers to membership queries (Q1369067) (← links)
- Decision tree induction based on efficient tree restructuring (Q1373712) (← links)
- Online learning versus offline learning (Q1373713) (← links)
- Coping with uncertainty in map learning (Q1373714) (← links)
- On the optimality of the simple Bayesian classifier under zero-one loss (Q1380855) (← links)
- Bayesian network classifiers (Q1380857) (← links)
- The sample complexity of learning fixed-structure Bayesian networks (Q1380858) (← links)
- Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables (Q1380860) (← links)
- Adaptive probabilistic networks with hidden variables (Q1380863) (← links)
- Factorial hidden Markov models (Q1380864) (← links)
- Predicting protein secondary structure using stochastic tree grammars (Q1380866) (← links)
- Learning with probabilistic representations (Q1380867) (← links)
- 9th annual conference on Computational learning theory, COLT '96. Desenzano del Garda, Italy, June 28 - July 1, 1996 (Q1383188) (← links)
- PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples (Q1383190) (← links)
- A note on learning from multiple-instance examples (Q1383191) (← links)
- Strong minimax lower bounds for learning (Q1383192) (← links)
- Hardness results for learning first-order representations and programming by demonstration (Q1383193) (← links)
- On restricted-focus-of-attention learnability of Boolean functions (Q1383194) (← links)
- Variance and bias for general loss functions (Q1394781) (← links)
- Learning from different teachers (Q1394782) (← links)
- Microchoice bounds and self bounding learning algorithms (Q1394783) (← links)
- Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy (Q1394785) (← links)
- Boosting and hard-core set construction (Q1394786) (← links)
- Potential-based algorithms in on-line prediction and game theory (Q1394787) (← links)
