Download Explanation-Based Neural Network Learning: A Lifelong by Sebastian Thrun PDF

By Sebastian Thrun

Lifelong studying addresses occasions within which a learner faces a sequence of other studying projects delivering the chance for synergy between them. Explanation-based neural community studying (EBNN) is a laptop studying set of rules that transfers wisdom throughout a number of studying initiatives. whilst confronted with a brand new studying activity, EBNN exploits area wisdom collected in earlier studying initiatives to lead generalization within the new one. accordingly, EBNN generalizes extra effectively from much less facts than related tools. Explanation-Based Neural community studying: A Lifelong LearningApproach describes the elemental EBNN paradigm and investigates it within the context of supervised studying, reinforcement studying, robotics, and chess.
`The paradigm of lifelong studying - utilizing previous discovered wisdom to enhance next studying - is a promising course for a brand new new release of computer studying algorithms. Given the necessity for extra actual studying tools, it really is tricky to visualize a destiny for computer studying that doesn't comprise this paradigm.'
From the Foreword by way of Tom M. Mitchell.

Show description

Read Online or Download Explanation-Based Neural Network Learning: A Lifelong Learning Approach PDF

Best artificial intelligence books

Elements of Artificial Intelligence: Introduction Using LISP (Principles of computer science series)

The breadth of insurance is greater than sufficient to offer the reader an outline of AI. An creation to LISP is located early within the booklet. even though a supplementary LISP textual content will be a good idea for classes within which wide LISP programming is needed, this bankruptcy is adequate for novices who're usually in following the LISP examples discovered later within the publication.

Fuzzy Knowledge Management for the Semantic Web (Studies in Fuzziness and Soft Computing)

This publication is going to nice intensity in regards to the quickly transforming into subject of applied sciences and methods of fuzzy common sense within the Semantic net. the subjects of this e-book comprise fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology wisdom bases, extraction of fuzzy description logics and ontologies from fuzzy information versions, garage of fuzzy ontology wisdom bases in fuzzy databases, fuzzy Semantic net ontology mapping, and fuzzy ideas and their interchange within the Semantic internet.

The Computer and the Brain (3rd Edition) (The Silliman Memorial Lectures Series)

Writer notice: ahead via Ray Kurzweil

In this vintage paintings, one of many maximum mathematicians of the 20 th century explores the analogies among computing machines and the residing human mind. John von Neumann, whose many contributions to technology, arithmetic, and engineering contain the fundamental organizational framework on the center of today's desktops, concludes that the mind operates either digitally and analogically, but additionally has its personal odd statistical language.

In his foreword to this re-creation, Ray Kurzweil, a futurist well-known partially for his personal reflections at the courting among know-how and intelligence, areas von Neumann’s paintings in a ancient context and indicates the way it is still proper this day.

Dependency Structures and Lexicalized Grammars: An Algebraic Approach

Considering the fact that 2002, FoLLI has provided an annual prize for amazing dissertations within the fields of common sense, Language and data. This ebook relies at the PhD thesis of Marco Kuhlmann, joint winner of the E. W. Beth dissertation award in 2008. Kuhlmann’s thesis lays new theoretical foundations for the research of non-projective dependency grammars.

Extra resources for Explanation-Based Neural Network Learning: A Lifelong Learning Approach

Example text

The SOAR architecture automatically records this explanation whenever it reflects in this fashion, and the chunking mechanism forms a new control rule (called a production) by collecting the features mentioned in the explanation into the preconditions of the new rule. SOAR's analytical chunking mechanism has been shown to learn successfully to speed up problem solving across a broad range of domains. For example, [60] presents results in which over 100,000 productions are learned from such explanations within one particular domain.

In some cases, intermediate concepts of the explanation are directly observable. In this case, the prediction accuracy can be estimated independently for each individual domain theory network. EBNN assigns an individual error value dj (p) to each individual inference step j. Now assume the slope is a product of m(p) chained slopes, each having its own error dj(p) (j = 1, ... , m). 17) max This product assigns large weight only to slopes where every domain theory network exhibits a small prediction error.

On the other hand, they typically require large amounts Explanation-Based Neural Network Learning 21 (a) Target concept. open_vessel/\ flaLbottom /\ [( is_light 1\ has-handle) V (made_of_Styrofoam 1\ upward_concave)) (b) Training examples. IS light has handle made of Styrofoam color upward concave open vessel fiat bottom IS expensive . v IS_CUp. 1 Cup example. (a) The target concept. (b) Training examples. of training data to generalize correctly. To illustrate this, consider the cup example. 1.

Download PDF sample

Rated 4.58 of 5 – based on 5 votes