Generalization and learning error for nonlinear perceptron

Perceptron-like Algorithms and Generalization Bounds for Learning to Rank

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of t...

A Generalization Error Estimate for Nonlinear Systems

Training Error, Generalization Error and Learning Curves in Neural Learning

A ,neural ‘nM?tUJOrk is tro.in,ed by using CL set of ~Z’lJaib nble exn.m,ples to minimize the twining error sv.ch th,nt the network pnra,meters ,fit the eznmples well. However, it is desired to min.imize the generalization error to which no direct access is possible. There are discrepa,ncies between the training error an.d the gen,eralization error due to th,e statistical fluctuation of exnmple...

A Generalization Error for Q-Learning

Planning problems that involve learning a policy from a single training set of finite horizon trajectories arise in both social science and medical fields. We consider Q-learning with function approximation for this setting and derive an upper bound on the generalization error. This upper bound is in terms of quantities minimized by a Q-learning algorithm, the complexity of the approximation sp...

Generalization and Regularization in Nonlinear Learning Systems