Download e-book for iPad: Analysis and Design of Machine Learning Techniques: by Patrick Stalph

By Patrick Stalph

ISBN-10: 3658049367

ISBN-13: 9783658049362

ISBN-10: 3658049375

ISBN-13: 9783658049379

Manipulating or greedy items sounds like a trivial job for people, as those are motor talents of way of life. however, motor talents usually are not effortless to benefit for people and this is often additionally an energetic study subject in robotics. notwithstanding, so much recommendations are optimized for commercial functions and, hence, few are believable reasons for human studying. the basic problem, that motivates Patrick Stalph, originates from the cognitive technology: How do people study their motor talents? the writer makes a connection among robotics and cognitive sciences through studying motor ability studying utilizing implementations which may be present in the human mind – no less than to some degree. for that reason 3 compatible laptop studying algorithms are chosen – algorithms which are believable from a cognitive point of view and possible for the roboticist. the ability and scalability of these algorithms is evaluated in theoretical simulations and extra life like situations with the iCub humanoid robotic. Convincing effects make sure the applicability of the method, whereas the organic plausibility is mentioned in retrospect.

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Additional info for Analysis and Design of Machine Learning Techniques: Evolutionary Solutions for Regression, Prediction, and Control Problems

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3 Artificial Neural Networks Inspired by the capabilities of the human brain, interest in simulated neurons dates back to 1943 [61]. 4 Non-Parametric Regression 21 receives inputs and produces some kind of output. Thus, a network of neurons, so called Artificial Neural Network (ANN), is clearly related to FA as well. Furthermore, biological plausibility comes for free with such models, when necessary simplifications to simulate neural activity in a computer are ignored. The Cybenko theorem [23] states that any continuous function can be approximated with arbitrary precision on a (compact) subset of Rn with a particularly simple type of ANN.

In its area of responsibility a simple local model is learned by an iterative gradient descent. Thus the method works online and, furthermore, with a low computational demand. The major drawback of RBFNs is the missing training possibilities for the kernels. 28 Chapter 2 Introduction to Function Approximation and Regression This issue is addressed by two local learning algorithms, namely LWPR and XCSF. Similar to RBFNs those algorithms iteratively model non-linear functions with several local models whose area of responsibility is defined by a kernel.

It is non-trivial to find the optimal rotation parameters for a regression problem at hand, but there are much richer options to cluster the input space. Methods to optimize such a complex shape will be introduced in a later chapter. 2 Alternative Shapes The use of a quadratic distance divided by squared radii results in an ellipsoid equation. 4.

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Analysis and Design of Machine Learning Techniques: Evolutionary Solutions for Regression, Prediction, and Control Problems by Patrick Stalph

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