Pages that link to "Item:Q1881402"
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The following pages link to New approaches to statistical learning theory (Q1881402):
Displaying 22 items.
- An oracle inequality for regularized risk minimizers with strongly mixing observations (Q373424) (← links)
- Generalization bounds of ERM algorithm with Markov chain samples (Q403479) (← links)
- Learning from regularized regression algorithms with \(p\)-order Markov chain sampling (Q423185) (← links)
- Generalization bounds of ERM algorithm with \(V\)-geometrically ergodic Markov chains (Q429786) (← links)
- Rethinking statistical learning theory: learning using statistical invariants (Q669285) (← links)
- Rigorous learning curve bounds from statistical mechanics (Q676243) (← links)
- Embedding sample points uncertainty measures in learning algorithms (Q1003548) (← links)
- Learning from uniformly ergodic Markov chains (Q1023402) (← links)
- Semi-supervised learning based on high density region estimation (Q1784700) (← links)
- Complexity regularization via localized random penalties (Q1879970) (← links)
- The generalization performance of ERM algorithm with strongly mixing observations (Q1959486) (← links)
- Adaptive learning rates for support vector machines working on data with low intrinsic dimension (Q2073699) (← links)
- Special issue on new theoretical challenges in machine learning (Q2345945) (← links)
- The performance bounds of learning machines based on exponentially strongly mixing sequences (Q2458710) (← links)
- Aspects of discrete mathematics and probability in the theory of machine learning (Q2478432) (← links)
- Distinctive features of minimization of a risk functional in mass data sets (Q2487909) (← links)
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- (Q4998863) (← links)
- (Q5091718) (← links)
- Parallel learning -- a new framework for machine learning (Q5367546) (← links)
- Refined Rademacher Chaos Complexity Bounds with Applications to the Multikernel Learning Problem (Q5378343) (← links)
- Learning with Limited Samples: Meta-Learning and Applications to Communication Systems (Q5886000) (← links)