In the presence of nuisance parameters, we derive an explicit higher order asymptotic formula to compare the expected lengths of conndence intervals given by likelihood ratio statistics arising from the usual proole likelihood and various adjustments thereof. Highest posterior density regions, with approximate frequentist validity, are also included in the study.

We consider safety games on finite, edge-labeled graphs and present an algorithm based on automata learning to compute small strategies. Our idea is as follows: we incrementally learn regular sets of winning plays until a winning strategy can be derived. For this purpose we develop a modified version of Kearns and Vazirani’s learning algorithm. Since computing a minimal strategy in this setting...

The “fire together, wire together” Hebbian learning model is a central principle in neuroscience, but, surprisingly, it has found limited applicability in modern machine learning. In this paper, we show that neuro-plausible variants of competitive Hebbian learning provide a promising foundation for bottom-up deep learning. We propose an unsupervised learning algorithm termed Adaptive Hebbian Le...

The evolutionary learning of fuzzy neural networks (FNN) consists of structure learning to determine the proper number of fuzzy rules and parameters learning to adjust the network parameters. Many optimization algorithms can be applied to evolve FNN. However the search space of most algorithms has fixed dimension, which cannot suit to dynamic structure learning of FNN. We propose a novel techni...

A crucial issue in reinforcement learning applications is how to set meta-parameters, such as the learning rate and ”temperature” for exploration, to match the demands of the task and the environment. In this thesis, a method to adjust meta-parameters of reinforcement learning by using a real-number genetic algorithm is proposed. Simulations of foraging tasks show that appropriate settings of m...

Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the...

The large amount of computation necessary for obtaining time optimal solution for moving a manipulator on specified path has made it impossible to introduce an on line time optimal control algorithm. Most of this computational burden is due to calculation of switching points. In this paper a learning algorithm is proposed for finding the switching points. The method, which can be used for both ...

In this paper, the idea of the neuro-fuzzy learning algorithm has been extended, by which the tuning parameters in the fuzzy rules can be learned without changing the fuzzy rule table form used in usual fuzzy applications. A new neuro-fuzzy learning algorithm in the case of the fuzzy singleton-type reasoning method has been proposed. Due to the flexibility of the fuzzy singleton-type reasoning ...

cerebellar model articulation controller neural network is a computational model of cerebellum which acts as a lookup table. the advantages of cmac are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. in the training phase, the disadvantage of some cmac models is unstable phenomenon...