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72,546
Discovering Inductive Biases in Categorization through Iterated Learning
"... Progress in studying human categorization has typically involved comparing generalization judgments made by people to those made by models for a variety of training conditions. In this paper, we explore an alternative method for understanding human category learning—iterated learning—which can direc ..."
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Cited by 1 (1 self)
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directly expose the inductive biases of human learners and categorization models. Using a variety of stimulus sets, we compare the results of iterated learning experiments with human learners to results from two prominent classes of computational models: prototype models and exemplar models. Our results
Boosting to Correct Inductive Bias in Text Classification
"... This paper studies the effects of boosting in the context of different classification methods for text categorization, including Decision Trees, Naive Bayes, Support Vector Machines (SVMs) and a Rocchiostyle classifier. We identify the inductive biases of each classifier and explore how boosting, a ..."
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This paper studies the effects of boosting in the context of different classification methods for text categorization, including Decision Trees, Naive Bayes, Support Vector Machines (SVMs) and a Rocchiostyle classifier. We identify the inductive biases of each classifier and explore how boosting
Grammatical Acquisition: Inductive Bias and Coevolution of Language and the Language Acquisition Device
 Language
, 2000
"... An account of grammatical acquisition is developed within the parametersetting framework applied to a generalized categorial grammar (GCG). The GCG is embedded in a default inheritance network yielding a natural partial ordering (reflecting generality) of parameters which determines a partial ord ..."
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Cited by 60 (0 self)
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on acquisition of inductive bias, that is, of differing initial parameter settings, are explored via computational simulation. Computational simulation of populations of language learners and users instantiating the acquisition model show: 1) that variant acquisition procedures, with differing inductive
Learning from games: Inductive bias and Bayesian inference
 In N. Taatgen & H. van Rijk (Eds.), Proceedings of the 31st annual conference of the Cognitive Science Society (pp. 2729– 2734). Austin, TX: Cognitive Science Society
, 2009
"... A classic problem in understanding human intelligence is determining how people make inductive inferences when presented with small amounts of data. We examine this question in the context of the guessthenextnumber game, where players are presented with short series of numbers and asked to guess ..."
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Cited by 2 (0 self)
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of the mathematical inductive bias of our sample population. We then use this framework to solve novel sequence guessing problems computationally, mirroring human performance. Our goal is to better understand how people approach math problems by examining the space of mathematical functions they find easiest to both
Inductive Bias in CaseBased Reasoning Systems
 Department of Computer Science, University of York, York
, 1995
"... In order to learn more about the behaviour of casebased reasoners as learning systems, we formalise a simple casebased learner as a PAC learning algorithm, using the casebased representation hCB; oei. We first consider a `naive' casebased learning algorithm CB1(oeH ) which learns by collec ..."
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Cited by 2 (1 self)
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to characterise CB1(oeH ) as a `weak but general' learning algorithm. We then consider how the sample complexity of casebased learning can be reduced for specific classes of target concept by the application of inductive bias, or prior knowledge of the class of target concepts. Following recent work
Cultural Transmission and Inductive Biases in Populations of Bayesian Learners
"... Recent research on computational models of language change and cultural evolution in general has focused on the analytical study of languages as dynamic systems, thus avoiding the difficulties of analysing the complex multiagent interactions underlying numerical simulations of cultural transmission ..."
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transmission. The same is true for the examination of the effects of inductive biases on language distributions within the Bayesian Iterated Learning Framework. The aim of this work is to test whether the strong results obtained through analytical methods in this framework also extend to finite populations
Inductive Bias in ContextFree Language Learning
 IN PROCEEDINGS OF THE NINTH AUSTRALIAN CONFERENCE ON NEURAL NETWORKS
, 1998
"... Recurrent neural networks are capable of learning contextfree tasks, however learning performance is unsatisfactory. We investigate the effect of biasing learning towards finding a solution to a contextfree prediction task. The first series of simulations #xes various sets of weights of the net ..."
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Cited by 20 (10 self)
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Recurrent neural networks are capable of learning contextfree tasks, however learning performance is unsatisfactory. We investigate the effect of biasing learning towards finding a solution to a contextfree prediction task. The first series of simulations #xes various sets of weights
Rule Extraction from KnowledgeBased Neural Networks with Adaptive Inductive Bias
, 2001
"... The integration of symbolic knowledge with artificial neural networks is becoming an increasingly popular paradigm for solving realworld applications. The paradigm provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bia ..."
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Cited by 3 (1 self)
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the strength of the inductive bias which takes the network architecture, the prior knowledge, the training data, and the learning algorithm into consideration; networks trained with adaptive inductive bias showed superior performance over networks trained with a standard inductive bias. This paper compares
Converting Semantic MetaKnowledge into Inductive Bias
 In Proceedings of the 15th International Conference on Inductive Logic Programming
, 2005
"... Abstract. The Cyc KB has a rich preexisting ontology for representing common sense knowledge. To clarify and enforce its terms ’ semantics and to improve inferential efficiency, the Cyc ontology contains substantial metalevel knowledge that provides definitional information about its terms, such a ..."
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Cited by 5 (3 self)
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, such as a type hierarchy. This paper introduces a method for converting that metaknowledge into biases for ILP systems. The process has three stages. First, a “focal position ” for the target predicate is selected, based on the induction goal. Second, the system determines type compatibility or conflicts
What inductive bias gives good neural network training performance
 In Proc. IEEEINNSENNS Int. Joint Conf. Neural Networks (IJCNN'00
, 2000
"... There has been an increased interest in the use of prior knowledge for training neural networks. Prior knowledge in the form of Horn clauses has been the predominant paradigm for knowledgebased neural networks. Given a set of training examples and an initial domain theory, a neural network is const ..."
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Cited by 10 (4 self)
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is constructed that fits the training examples by preprogramming some of the weights. The initialized neural network is then trained using backpropagation to refine the knowledge. This paper proposes a heuristic for determining the strength of the inductive bias by making use of gradient information in weight
Results 11  20
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72,546