Download e-book for kindle: Learning to Learn by Sebastian Thrun, Lorien Pratt
By Sebastian Thrun, Lorien Pratt
Over the earlier 3 a long time or so, learn on computing device studying and information mining has resulted in a large choice of algorithms that research common services from adventure. As desktop studying is maturing, it has all started to make the profitable transition from educational learn to varied functional functions. typical suggestions reminiscent of determination timber and synthetic neural networks, for instance, are actually getting used in a variety of advertisement and commercial purposes.
studying to profit is a thrilling new learn course inside of laptop studying. just like conventional machine-learning algorithms, the equipment defined in Learning to Learn set off basic features from adventure. despite the fact that, the e-book investigates algorithms which could switch the way in which they generalize, i.e., perform the duty of studying itself, and increase on it.
to demonstrate the application of studying to profit, it truly is invaluable evaluating computer studying with human studying. people come upon a continuous movement of studying initiatives. they don't simply research options or motor abilities, in addition they examine bias, i.e., they generalize. hence, people are usually capable of generalize safely from tremendous few examples - usually only a unmarried instance suffices to educate us a brand new factor.
A deeper figuring out of computing device courses that enhance their skill to benefit could have a wide sensible influence at the box of computer studying and past. lately, the sphere has made major growth in the direction of a thought of studying to profit besides sensible new algorithms, a few of which resulted in awesome leads to real-world purposes.
Learning to Learn offers a survey of a few of the main fascinating new examine techniques, written by means of prime researchers within the box. Its goal is to enquire the software and feasibility of laptop courses that could the right way to examine, either from a pragmatic and a theoretical element of view.
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Extra resources for Learning to Learn
4. Consolidation: the new task is stored in such a way as to facilitate recognition and use in future tasks. Overall, there is far less focus on recognition in connectionist efforts than in analogy. Consolidation usually occurs, but not as a separate step from the consolidation of the training data that happens anyway during source learning. Exceptions are [Robins, 1996a] and [Silver and Mercer, 1995], who show how a network can learn to consolidate training information from other networks. There is also not usually as clear a distinction between the elaboration and evaluation phases in connectionist systems as in analogy.
This facilitates positive transfer. 3 Abu Mostafa: Learning from Hints [Abu-Mostafa, 1994] describes a mechanism for supplementing a training set through examples that are generated to be consistent with a set of rules, called here hints. Examples of hints are symmetry and monotonicity. Tests on financial data show statistically significantly improved learning curves with such supplemented training sets. PRATr & JENNINGS: A SURVEY OF CONNECTIONIST NETWORK REUSE. . 35 This is a functional transfer method because both hint examples and normal training examples are learned simultaneously.
Second tier for decision trees. In Proceedings of the 13th international conference on machine learning, Bari, Italy, 1996. Morgan Kaufmann. M. Lando and S. Edelman. Receptive field spaces and class-based generalization from a single view in face recognition. Network, 6:551-576, 1995. Gale Martin. The effects of old learning on new in Hopfield and backpropagation nets. Technical Report ACA-HI-019, Microelectronics and Computer Technology Corporation (MCC), 1988. PRATT & JENNINGS: A SURVEY OF CONNECTIONIST NETWORK REUSE.
Learning to Learn by Sebastian Thrun, Lorien Pratt