Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collabora...

A constant element of our modern environment is change. In decision-making research however, very little is known about how people make choices in dynamic environments. We report the results of an experiment where participants were asked to choose between two options: a dynamic and risky option that resulted in either a high or a low outcome, and a stationary and safe option that resulted in a ...

In this paper we describe the application of Memory Based Learning to the problem of Prepositional Phrase attachment disam biguation We compare Memory Based Learning which stores examples in mem ory and generalizes by using intelligent sim ilarity metrics with a number of recently proposed statistical methods that are well suited to large numbers of features We evaluate our methods on a common ...

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and t...

We present a cognitive model performing the Dynamic Stocks&Flows control task, in which subjects control a system by counteracting a systematically changing external variable. The model uses a metacognitive layer that chooses a task strategy drawn from of two classes of strategies: precise calculation and imprecise estimation. The model, formulated within the ACT-R theory, monitors the success ...

This paper addresses the issue of reducing the storage requirements on Instance-Based Learning algorithms. Algorithms proposed by other researches use heuristics to prune instances of the training set or modify the instances themselves to achieve a reduced set of instances. Our work presents an alternative way. We propose to induce a reduced set of partially-defined instances with Evolutionary ...

This paper reports the results of experiments using memory-based learning to guide a deterministic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, memory-based classifiers for predicting the next action of the parser are constructed. The accuracy of a classifier as such is evaluated on held-out data derived from the treebank, and its perfor...

Instance-based learning methods like the nearest neighbour classiÿer generally suuer from the indiscriminate storage of all training instances, resulting in large memory requirements and slow execution speed. In this paper, new training set size reduction methods based on prototype generation and space partitioning are proposed. Experimental results show that the new algorithms achieve a very h...

We present mathematical learning models—predictions of student’s knowledge vs amount of instruction— that are based on assumptions motivated by various theories of learning: tabula rasa, constructivist, and tutoring. These models predict the improvement on the post-test as a function of the pretest score due to intervening instruction and also depend on the type of instruction. We introduce a c...

This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the PL model to fit locally constant probability models in the context of instance-based learning. As opposed to this, the second method estimates a global model in which the PL parameters are represented as functions of t...