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1	Inductive generalisation in case-based reasoning systems Griffiths, Anthony D. January 1996 (has links) No description available. 005
2	Measure-based Learning Algorithms : An Analysis of Back-propagated Neural Networks Khalid, Fahad January 2008 (has links) In this thesis we present a theoretical investigation of the feasibility of using a problem specific inductive bias for back-propagated neural networks. We argue that if a learning algorithm is biased towards optimizing a certain performance measure, it is plausible to assume that it will generate a higher performance score when evaluated using that particular measure. We use the term measure function for a multi-criteria evaluation function that can also be used as an inherent function in learning algorithms, in order to customize the bias of a learning algorithm for a specific problem. Hence, the term measure-based learning algorithms. We discuss different characteristics of the most commonly used performance measures and establish similarities among them. The characteristics of individual measures and the established similarities are then correlated to the characteristics of the backpropagation algorithm, in order to explore the applicability of introducing a measure function to backpropagated neural networks. Our study shows that there are certain characteristics of the error back-propagation mechanism and the inherent gradient search method that limit the set of measures that can be used for the measure function. Also, we highlight the significance of taking the representational bias of the neural network into account when developing methods for measure-based learning. The overall analysis of the research shows that measure-based learning is a promising area of research with potential for further exploration. We suggest directions for future research that might help realize measure-based neural networks. / The study is an investigation on the feasibility of using a generic inductive bias for backpropagation artificial neural networks, which could incorporate any one or a combination of problem specific performance metrics to be optimized. We have identified several limitations of both the standard error backpropagation mechanism as well the inherent gradient search approach. These limitations suggest exploration of methods other than backpropagation, as well use of global search methods instead of gradient search. Also, we emphasize the importance of taking the representational bias of the neural network in consideration, since only a combination of both procedural and representational bias can provide highly optimal solutions. Supervised learning Inductive Bias Artificial Neural Networks Computer Sciences Datavetenskap (datalogi)
3	A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization Carroll, James Lamond 10 March 2010 (has links) (PDF) Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or EBF. This model is descriptive, in that it can describe and compare learning algorithms. Thus the EBF is capable of modeling both effective and ineffective learning algorithms. We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of the EBF to deal with non-deterministic outcomes, and with utility functions other than 0-1 loss. Finally, we modify the EBF to create a "prescriptive" learning model, meaning that, instead of describing existing algorithms, our model defines how learning should optimally take place. We call the resulting model the Unified Bayesian Decision Theoretical Model, or the UBDTM. WE show that this model can serve as a cohesive theory and framework in which a broad range of questions can be analyzed and studied. Such a broadly applicable unified theoretical framework is one of the major missing ingredients of machine learning theory. Using the UBDTM, we concentrate on supervised learning and empirical function optimization. We then use the UBDTM to reanalyze many important theoretical issues in Machine Learning, including No-Free-Lunch, utility implications, and active learning. We also point forward to future directions for using the UBDTM to model learnability, sample complexity, and ensembles. We also provide practical applications of the UBDTM by using the model to train a Bayesian variation to the CMAC supervised learner in closed form, to perform a practical empirical function optimization task, and as part of the guiding principles behind an ongoing project to create an electronic and print corpus of tagged ancient Syriac texts using active learning. machine learning supervised learning function optimization empirical function optimization statistics Bayes Bayes Law Bayesian Bayesian learning decision theory utility theory unified Bayesian model UBM UBDTM learning framework no-free-lunch NFL a priori learning extended Bayesian formalism EBF bias inductive bias hypothesis space function class active learning uncertainty sampling query by uncertainty query by committee expected value of sample information EVSI Computer Sciences

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