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Showing 1 to 20 of 24 for “"rule learning"”.
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Cooperative rule-learning: a metacognitive interpretation
… of reasoning, and working with a partner, on rule-learning tasks were investigated. Subjects completed eight rule-learning problems, while working alone or as pairs. The trials to solution, proportion of untenable hypotheses, strategy efficiency, and decision time were assessed in both a …
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Random Forests Based Rule Learning And Feature Elimination
… that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. We propose an efficient approach, combining rule extraction and feature elimination, based on 1-norm regularized random forests. This approach …
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A Rule Learning Application of the Theory of Propositional Learning
Made available in DSpace on 2014-12-13T18:21:31Z (GMT). No. of bitstreams: 1 7606682.pdf: 8960941 bytes, checksum: 5d919e7ecaf92185077ee63f9ded789b (MD5) Previous issue date: 1975
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An Approach For Scalable First-Order Rule Learning On Twitter Data
Scalable Rule Learning (SRLearn) is a scalable divide-and-conquer approach with graph-based modeling of social media data, to scale up first-order rule learning through Markov Logic Networks on a commodity cluster on large scale Twitter data. SRLearn takes advantage of distributed systems to …
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The effect of acute physical activity on memory for vocabulary and linguistic rule learning
… that targeted either vocabulary or linguistic rule learning. Vocabulary items were taught explicitly, in a manner thought to engage declarative memory systems. The rule-learning task, on the other hand, combined an explicit rule with an implicitly-taught rule, meant to be learnt by repeated …
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Spatial Learning and Memory, Cognitive Flexibility, and Rule-Learning in Food-Caching Mountain Chickadees (Poecile gambeli) Across an Elevational Gradient
… highly advantageous, as they are involved with rule learning and using abstract, relational concepts; however, they remain highly controversial in nonhuman animals and have rarely been studied outside of a laboratory context. In this dissertation, I designed and conducted several spatial …
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Exploring Accessibility and Companionship in Boardgames via Alexa
The inaccessibility of rulebooks hinders the rule learning experience of boardgame players who are blind or low vision (BLV). We explore the design of conversational agents (CAs) to support players' learning needs and provide companionship by conducting two qualitative studies. In study 1, 14 …
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Learning probabilistic relational dynamics for multiple tasks
While large data sets have enabled machine learning algorithms to act intelligently in complex domains, standard machine learning algorithms perform poorly in situations in which little data exists for the desired target task. Transfer learning attempts to extract trends from the data of similar …
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Learning meaning in Genesis
… about 1000 lines of Java code representing 31 rules to turn English sentences into a variety of more meaningful semantic representations. I reproduced the functionality of these rules by training the existing rule-learning program UNDERSTAND with 43 human-readable examples of English sentences …
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Blending cognitive rule-based, process-based, and context-based theories in the development of online grammar instruction
… in higher education, given the complexity of the rule learning that was being asked of them. By blending approaches from tested educational research on cognitive information processing theories, schema theories, and situated cognitive theories in order to determine how language rules are best …
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Reducts and Rough Set Analysis
… mining. It can be used to learn classification rules that define classes of a classi- fication based on some well defined concepts. The fundamental task of rough set data analysis is to precisely construct and interpret concepts. When applying rough set theory to rule learning, the main tasks …
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CREATE: Clinical Record Analysis Technology Ensemble
… winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new …
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Learning to Plan by Learning Rules
Many environments involve following rules and tasks; for example, a chef cooking a dish follows a recipe, and a person driving follows rules of the road. People are naturally fluent with rules: we can learn rules efficiently; we can follow rules; we can interpret rules and explain them to others; …
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New group testing paradigms: from practice to theory
… to other applications such as interpretable rule learning for decision making. Semi-quantitative group testing (SQGT) is a (possibly) non-binary pooling scheme that may be viewed as a concatenation of an adder channel and an integer-valued quantizer. In its full generality, SQGT may be viewed …
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Language acquisition by computer: Learning categories, agreement and morphology under psychological constraints
… CAM learns in a largely bottom-up manner, learning parts of categories first, then context-free grammar rules based on these categories, and finally agreemnt rules on top of the context-free grammar rules. CAM duplicates the partial order relations observed by R. Brown (Brown, 1973) in …
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Insider Threat Reduction Model for the cloud environment
… the cloud environment. The model uses sequential rule mining techniques to reason about the behaviour patterns of the user and predict whether a user is a normal user or a malicious user who has masqueraded in the system. A rule learning algorithm was developed and used in learning the behavior …
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Towards automated complex ontology alignment using rule-based machine learning
… ontology alignment system based on association rule learning to generate not only simple correspondences but also complex ones. The algorithm can also be used in a semi-automated fashion to effectively assist users in finding potential complex alignments that they can then validate or edit. …
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Individual Differences in Function Learning as They Relate to the Learning of Conceptual Information
… considered within the problem-solving or concept-learning literatures despite the indication that some individuals are better able to transfer to novel problems and that manipulations in strategy can effectively increase the ability to transfer: Gick & Holyoak, 1983). Research in the …
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Structured Rough Set Approximations
… areas, such as artificial intelligence, machine learning and data mining. Lower and upper approximations are two fundamental notions for concept analysis with rough set theory. In rough set theory, one can obtain two kinds of sets in an information table, namely, definable and undefinable sets. …
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Automatic concept learning via information lattices
"Concept learning is about distilling interpretable rules and concepts from data, a prelude to more advanced knowledge discovery and problem solving in creative domains such as art and science. While concept learning is pervasive in humans, current artificial intelligent (AI) systems are mostly …
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