The following pages link to Machine Learning (Q65106):
Displaying 50 items.
- Probabilistic combination of classification rules and its application to medical diagnosis (Q890315) (← links)
- A computational approach to nonparametric regression: bootstrapping CMARS method (Q890318) (← links)
- Committee polyhedral separability: complexity and polynomial approximation (Q890319) (← links)
- Triadic formal concept analysis and triclustering: searching for optimal patterns (Q890322) (← links)
- Random drift particle swarm optimization algorithm: convergence analysis and parameter selection (Q890323) (← links)
- Guest editors' introduction: Special issue on inductive logic programming and on multi-relational learning (Q894691) (← links)
- Probabilistic (logic) programming concepts (Q894692) (← links)
- Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (Q894695) (← links)
- Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (Q894696) (← links)
- Efficient inference and learning in a large knowledge base. Reasoning with extracted information using a locally groundable first-order probabilistic logic (Q894701) (← links)
- Bandit-based Monte-Carlo structure learning of probabilistic logic programs (Q894703) (← links)
- Implementing Valiant's learnability theory using random sets. (Q960433) (← links)
- Tracking the best hyperplane with a simple budget perceptron (Q1009215) (← links)
- DNF are teachable in the average case (Q1009216) (← links)
- Unconditional lower bounds for learning intersections of halfspaces (Q1009217) (← links)
- A primal-dual perspective of online learning algorithms (Q1009218) (← links)
- Logarithmic regret algorithms for online convex optimization (Q1009221) (← links)
- Competing with wild prediction rules (Q1009222) (← links)
- Active sampling for multiple output identification (Q1009223) (← links)
- Introduction to the special issue on COLT 2006 (Q1009224) (← links)
- A formal framework and extensions for function approximation in learning classifier systems (Q1009226) (← links)
- Feature selection via sensitivity analysis of SVM probabilistic outputs (Q1009227) (← links)
- A linear fit gets the correct monotonicity directions (Q1009229) (← links)
- Incorporating prior knowledge in support vector regression (Q1009230) (← links)
- QG/GA: a stochastic search for Progol (Q1009231) (← links)
- Compressing probabilistic Prolog programs (Q1009237) (← links)
- On the connection between the phase transition of the covering test and the learning success rate in ILP (Q1009240) (← links)
- ALLPAD: approximate learning of logic programs with annotated disjunctions (Q1009241) (← links)
- Generalized ordering-search for learning directed probabilistic logical models (Q1009243) (← links)
- Margin-based first-order rule learning (Q1009245) (← links)
- Guest editorial: Special issue on inductive logic programming (Q1009246) (← links)
- Inductive logic programming for gene regulation prediction (Q1009247) (← links)
- Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path (Q1009248) (← links)
- Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm (Q1009249) (← links)
- Inductive process modeling (Q1009252) (← links)
- A \(k\)-norm pruning algorithm for decision tree classifiers based on error rate estimation (Q1009253) (← links)
- Learning symmetric causal independence models (Q1009256) (← links)
- Layered critical values: a powerful direct-adjustment approach to discovering significant patterns (Q1009257) (← links)
- Learning \((k,l)\)-contextual tree languages for information extraction from web pages (Q1009259) (← links)
- Learning the structure of dynamic Bayesian networks from time series and steady state measurements (Q1009260) (← links)
- Efficient approximate leave-one-out cross-validation for kernel logistic regression (Q1009261) (← links)
- On reoptimizing multi-class classifiers (Q1009262) (← links)
- U-shaped, iterative, and iterative-with-counter learning (Q1009263) (← links)
- Learning large-alphabet and analog circuits with value injection queries (Q1009264) (← links)
- Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity (Q1009266) (← links)
- Sketching information divergences (Q1009269) (← links)
- Robust reductions from ranking to classification (Q1009271) (← links)
- A theory of learning with similarity functions (Q1009272) (← links)
- Regret to the best vs. regret to the average (Q1009274) (← links)
- Guest editors' introduction: Special issue on learning theory (COLT-2007) (Q1009275) (← links)
