Margin-based first-order rule learning

A Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems

Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and  interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the pro...

First-Order rule learning through a Pulsed Neural Network

Destructive Rule-Based Properties and First-Order Logic

We study properties characterized by applying successively a “destructive” rule expressed in first-order logic. The rule says that points a1, ..., ak of a structure can be removed if they satisfy a certain firstorder formula φ(a1, ..., ak). The property defined this way by the formula is the set of finite structures such that we are able to obtain the empty structure when applying the rule repe...

Margin Based Active Learning

We present a framework for margin based active learning of linear separators. We instantiate it for a few important cases, some of which have been previously considered in the literature. We analyze the effectiveness of our framework both in the realizable case and in a specific noisy setting related to the Tsybakov small noise condition.

A rule-based presentation order facilitates category learning.

We investigated the mechanisms by which concepts are learned from examples by manipulating the presentation order in which the examples were presented to subjects. We introduce the idea of a rule-based presentation order, which is a sequence that respects the internal organization of the examples within a category. We find that such an order substantially facilitates learning, as compared with ...