We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate...

The organizational structure of a network is investigated with a simulated precipitation model which does not make use of prior knowledge about the community structure of the network. The result is presented as a structure profile through which various definitions of communities can be applied...

Purpose – The paper provides a theory base for deriving connotative descriptors for photographs from existing denotative descriptors, and then demonstrates a model for enhancing browsing within image collections by providing a tool for carving up the searching space....

Purpose – The purpose of this paper is to present a novel image‐labeling CMOS sensor for modulated marker detection. Design/methodology/approach – An image scene with multiple objects, each identified by a flashing light‐emitting diode (LED), is captured by the sensor. The LED's...

This paper investigates the use, for the task of classifier learning in the presence of misclassification costs, of some gradient descent style leveraging approaches to classifier learning: Schapire and Singer's AdaBoost.MH and AdaBoost.MR [16], and Collins et al's multi-class logistic...

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP,...