Pages that link to "Item:Q1198550"

The following pages link to Decision theoretic generalizations of the PAC model for neural net and other learning applications (Q1198550):

Displaying 50 items.

• A new approach for learning belief networks using independence criteria (Q1605675) (← links)
• Orthogonal series estimates on strong spatial mixing data (Q1681044) (← links)
• Relative deviation learning bounds and generalization with unbounded loss functions (Q1714946) (← links)
• Covering numbers for bounded variation functions (Q1791562) (← links)
• On weak base hypotheses and their implications for boosting regression and classification (Q1848929) (← links)
• On the complexity of learning for spiking neurons with temporal coding. (Q1854302) (← links)
• Improved lower bounds for learning from noisy examples: An information-theoretic approach (Q1854425) (← links)
• Learning fixed-dimension linear thresholds from fragmented data (Q1854473) (← links)
• Rates of uniform convergence of empirical means with mixing processes (Q1871232) (← links)
• Concentration inequalities, large and moderate deviations for self-normalized empirical processes (Q1872304) (← links)
• Some connections between learning and optimization (Q1885804) (← links)
• Efficient algorithms for learning functions with bounded variation (Q1887165) (← links)
• Sample sizes for threshold networks with equivalences (Q1891134) (← links)
• A counterexample concerning uniform ergodic theorems for a class of functions (Q1897079) (← links)
• Learning from a population of hypotheses (Q1900976) (← links)
• Relation between weight size and degree of over-fitting in neural network regression (Q1931976) (← links)
• Approximation with neural networks activated by ramp sigmoids (Q1958429) (← links)
• Incentive compatible regression learning (Q1959425) (← links)
• An inequality for uniform deviations of sample averages from their means (Q1962162) (← links)
• Analysis of two gradient-based algorithms for on-line regression (Q1970199) (← links)
• Surrogate losses in passive and active learning (Q2008623) (← links)
• Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space (Q2044364) (← links)
• Sharp estimates for the covering numbers of the Weierstrass fractal kernel (Q2099268) (← links)
• Primal and dual combinatorial dimensions (Q2112665) (← links)
• PALO: a probabilistic hill-climbing algorithm (Q2171267) (← links)
• A Bayesian perspective of statistical machine learning for big data (Q2203387) (← links)
• Best lower bound on the probability of a binomial exceeding its expectation (Q2244539) (← links)
• PAC-learning in the presence of one-sided classification~noise (Q2254605) (← links)
• Tight bounds on \(\ell_1\) approximation and learning of self-bounding functions (Q2290687) (← links)
• Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations (Q2305540) (← links)
• Exact lower bounds for the agnostic probably-approximately-correct (PAC) machine learning model (Q2328061) (← links)
• Probabilistic \(k\)-median clustering in data streams (Q2344214) (← links)
• Bracketing entropy and VC-dimension (Q2435838) (← links)
• Aspects of discrete mathematics and probability in the theory of machine learning (Q2478432) (← links)
• A fixed-distribution PAC learning theory for neural FIR models (Q2490395) (← links)
• Randomized algorithms for robust controller synthesis using statistical learning theory: a tutorial overview (Q2512151) (← links)
• Learning big (image) data via coresets for dictionaries (Q2513397) (← links)
• Approximation of Sobolev-type classes with quasi-seminorms (Q2566502) (← links)
• On the orders of nonlinear approximations for classes of functions of given form (Q2577348) (← links)
• Local Rademacher complexities (Q2583411) (← links)
• Non-linear approximation of functions with mixed smoothness by sets of finite pseudo-dimension (Q2641529) (← links)
• Learning cost-sensitive active classifiers (Q2676585) (← links)
• Learning half-spaces on general infinite spaces equipped with a distance function (Q2687990) (← links)
• On the mathematical foundations of learning (Q2761194) (← links)
• On the complexity of computing and learning with multiplicative neural networks (Q2780854) (← links)
• Optimal bounds on approximation of submodular and XOS functions by juntas (Q2816303) (← links)
• Learning hurdles for sleeping experts (Q2828218) (← links)
• Evolvability of real functions (Q2828219) (← links)
• Learning $$AC^0$$ Under k-Dependent Distributions (Q2988821) (← links)
• Deep-Learning Solution to Portfolio Selection with Serially Dependent Returns (Q3295874) (← links)