NDLI: Bridging logic and kernel machines

Content Provider	Springer Nature Link
Author	Diligenti, Michelangelo Gori, Marco Maggini, Marco Rigutini, Leonardo
Copyright Year	2011
Abstract	We propose a general framework to incorporate first-order logic (FOL) clauses, that are thought of as an abstract and partial representation of the environment, into kernel machines that learn within a semi-supervised scheme. We rely on a multi-task learning scheme where each task is associated with a unary predicate defined on the feature space, while higher level abstract representations consist of FOL clauses made of those predicates. We re-use the kernel machine mathematical apparatus to solve the problem as primal optimization of a function composed of the loss on the supervised examples, the regularization term, and a penalty term deriving from forcing real-valued constraints deriving from the predicates. Unlike for classic kernel machines, however, depending on the logic clauses, the overall function to be optimized is not convex anymore. An important contribution is to show that while tackling the optimization by classic numerical schemes is likely to be hopeless, a stage-based learning scheme, in which we start learning the supervised examples until convergence is reached, and then continue by forcing the logic clauses is a viable direction to attack the problem. Some promising experimental results are given on artificial learning tasks and on the automatic tagging of bibtex entries to emphasize the comparison with plain kernel machines.
Starting Page	57
Ending Page	88
Page Count	32
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	86
Issue Number	1
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2011-05-10
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Kernel machines First-order logic Learning from constraints Learning with prior knowledge Multi-task learning Semantic-based regularization Language Translation and Linguistics Simulation and Modeling Artificial Intelligence (incl. Robotics) Computing Methodologies Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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