The following pages link to A theory of the learnable (Q3714486):
Displaying 50 items.
- Unconfused ultraconservative multiclass algorithms (Q2353009) (← links)
- Learning linear PCA with convex semi-definite programming (Q2373456) (← links)
- On the Vapnik-Chervonenkis dimension of computer programs which use transcendental elementary operations (Q2379679) (← links)
- Discriminative learning can succeed where generative learning fails (Q2379956) (← links)
- Learning intersection-closed classes with signatures (Q2381578) (← links)
- Learning juntas in the presence of noise (Q2382279) (← links)
- On PAC learning algorithms for rich Boolean function classes (Q2382283) (← links)
- `Ideal learning' of natural language: positive results about learning from positive evidence (Q2382634) (← links)
- Guest editorial: Learning theory (Q2384140) (← links)
- Logical analysis of data -- the vision of Peter L. Hammer (Q2385443) (← links)
- Formal language identification: query learning vs. gold-style learning (Q2390327) (← links)
- Online learning of symbolic concepts (Q2403016) (← links)
- The unbounded-error communication complexity of symmetric functions (Q2428632) (← links)
- E-generalization using grammars (Q2457667) (← links)
- Some natural conditions on incremental learning (Q2461797) (← links)
- Learning with errors in answers to membership queries (Q2462498) (← links)
- The complexity of properly learning simple concept classes (Q2462500) (← links)
- Learning intersections of halfspaces with a margin (Q2462501) (← links)
- A general comparison of language learning from examples and from queries (Q2465036) (← links)
- Aspects of discrete mathematics and probability in the theory of machine learning (Q2478432) (← links)
- Finding the homology of submanifolds with high confidence from random samples (Q2482210) (← links)
- Exploring margin setting for good generalization in multiple class discrimination (Q2485070) (← links)
- Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms (Q2485074) (← links)
- Controlling the losing probability in a monotone game (Q2489238) (← links)
- Learning expressions and programs over monoids (Q2490112) (← links)
- A fixed-distribution PAC learning theory for neural FIR models (Q2490395) (← links)
- Improved bounds on quantum learning algorithms (Q2491374) (← links)
- Polynomial certificates for propositional classes (Q2495655) (← links)
- An algebra of human concept learning (Q2497765) (← links)
- The theoretical analysis of FDA and applications (Q2499135) (← links)
- An algorithmic theory of learning: Robust concepts and random projection (Q2499543) (← links)
- Connectionist computations of intuitionistic reasoning (Q2503271) (← links)
- The optimal PAC bound for intersection-closed concept classes (Q2512815) (← links)
- Regression conformal prediction with random forests (Q2512902) (← links)
- Cryptographic hardness for learning intersections of halfspaces (Q2517820) (← links)
- Efficient learning algorithms yield circuit lower bounds (Q2517822) (← links)
- Learning DNF from random walks (Q2568457) (← links)
- From Hopfield nets to recursive networks to graph machines: numerical machine learning for structured data (Q2575085) (← links)
- Learning erasing pattern languages with queries (Q2581361) (← links)
- Learning from positive and unlabeled examples (Q2581364) (← links)
- Prediction-hardness of acyclic conjunctive queries (Q2581365) (← links)
- Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels (Q2631344) (← links)
- Exact learning from an honest teacher that answers membership queries (Q2636406) (← links)
- Revisiting Shinohara's algorithm for computing descriptive patterns (Q2636407) (← links)
- Circuit lower bounds from learning-theoretic approaches (Q2636410) (← links)
- From equivalence queries to PAC learning: the case of implication theories (Q2658016) (← links)
- Minimizing depth of decision trees with hypotheses (Q2670891) (← links)
- Value iteration for simple stochastic games: stopping criterion and learning algorithm (Q2672267) (← links)
- Probably approximately optimal satisficing strategies (Q2674195) (← links)
- Noise modelling and evaluating learning from examples (Q2674201) (← links)
