The following pages link to (Q4892198):
Displaying 16 items.
- How to train a neural network. An introduction to the new computational paradigm. (Q960442) (← links)
- Decision theoretic generalizations of the PAC model for neural net and other learning applications (Q1198550) (← links)
- Worst-case properties of the uniform distribution and randomized algorithms for robustness analysis (Q1272556) (← links)
- A general frmework for supervised learning. Probably almost Bayesian algorithms (Q1309836) (← links)
- Probabilistic robustness analysis: Explicit bounds for the minimum number of samples (Q1391598) (← links)
- Learning theory applied to sigmoid network classification of protein biological function using primary protein structure (Q1422606) (← links)
- Statistical learning control of uncertain systems: theory and algorithms. (Q1854969) (← links)
- FIR Volterra kernel neural models and PAC learning (Q1860326) (← links)
- Sample sizes for threshold networks with equivalences (Q1891134) (← 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)
- \(H_{\infty}\) set membership identification: a survey (Q2576149) (← links)
- (Q3484380) (← links)
- Some notes on computational learning theory (Q3971255) (← links)
- The Principles of Deep Learning Theory (Q5070199) (← links)
- A general probabilistic formulation for supervised neural classifiers (Q5926455) (← links)
