Behavior in conflict situations can be influenced by the social information that individuals have about their opponents. This paper tests whether an existent Instance-based Learning (IBL) model, built using the Instance-based Learning Theory (IBLT) to explain behavior in a single-person binary-choice task (BCT), can predict behavior in a two-player iterated prisoner’s dilemma (IPD) game. The sa...

To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification ...

In real-world applications, it has been often observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. This effect becomes especially significant on instance-based learning due to the use of some dissimilarity measure. We analyz...

This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore , the theory indicates precisely how these conflicting demands must be balanced, in order to minimize cross-validation error....

Memory-based learning appears relatively successful when the learning data is highly disjunct, i.e., when classes are scattered over many small families of instances in instance space, as in many language learning tasks. Abstraction over borders of disjuncts tends to harm generalization performance. However , careful abstraction in memory-based learning may be harmless when it preserves the dis...

This paper presents a system for Semantic Role Labeling (SRL) for the CoNLL 2004 shared task (Carreras and Màrquez, 2004). The task is divided into two sub-tasks, recognition and labeling. These are performed independently with different feature representations. Both modules are based on the principle of memory-based learning. For the first module, we use the IOB2 format to determine whether a ...

An automatic method is proposed for the estimation of control parameters for musical synthesis algorithms. This method can be applied on a large class of systems for which a simulation, or “model” is available. In this paper we describe its application on a physical model of a trumpet where the “system” is an acoustical instrument, a trumpet, and the “model” a computer program that simulates tr...