By Jan Drugowitsch
This ebook offers a finished creation to the layout and research of studying Classifier platforms (LCS) from the point of view of computing device studying. LCS are a relatives of tools for dealing with unsupervised studying, supervised studying and sequential choice projects by way of decomposing higher challenge areas into easy-to-handle subproblems. opposite to typically impending their layout and research from the perspective of evolutionary computation, this ebook as a substitute promotes a probabilistic model-based method, in response to their defining query "What is an LCS speculated to learn?". Systematically following this process, it's proven how known laptop studying tools might be utilized to layout LCS algorithms from the 1st ideas in their underlying probabilistic version, that is during this e-book -- for illustrative reasons -- heavily with regards to the presently trendy XCS classifier approach. The strategy is holistic within the feel that the uniform goal-driven layout metaphor basically covers all features of LCS and places them on an effective starting place, as well as permitting the move of the theoretical starting place of many of the utilized desktop studying tools onto LCS. therefore, it doesn't simply strengthen the research of current LCS but additionally places ahead the layout of latest LCS inside of that very same framework.
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Additional info for Design and Analysis of Learning Classifier Systems: A Probabilistic Approach
Hence, we want to ﬁnd a model that represents the general pattern in the training data but does not model its noise. The ﬁeld that deals with this issue is known as model selection. Learning a model such that it perfectly ﬁts the training set but does not provide a good representation of f is known as overﬁtting. The opposite, that is, learning a model where the structural bias of the model dominates over the information included from the training set, is called underﬁtting. While in LCS several heuristics have been applied to deal with this issue, it has never been characterised explicitly.
In the model-free case, the function to model is the estimate of the value function, again leading to a regression task that needs to be handled incrementally. Additionally, the value function estimate is also updated incrementally, and as it is the data-generating process, this process is slowly changing. As a result, there is a dynamic interaction between the RL algorithm that updates the value function estimate and the incremental regression learner that models it, which is not in all cases stable and needs special consideration .
In a subsequent series of work [51, 44, 46, 58], Butz et al. derive various time and population bounds to analyse how XCS scales with the size of the input and the problem complexity, where the latter expresses how strongly the values of various input bits depend on each other. Combining these bounds, they show that the computational complexity of XCS grows linearly with respect to the input space size and exponentially with the problem complexity. Thus they state that XCS is a Probably Approximately Correct (PAC)5 learner .