This paper investigates incremental multiagent learning in static or evolving structured networks. Learning examples are incrementally distributed among the agents, and the object...
Gauvain Bourgne, Amal El Fallah-Seghrouchni, Henry...
Using data from an existing pre-algebra computer-based tutor, we analyzed the covariance of item-types with the goal of describing a more effective way to assign skill labels to it...
Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koe...
Abstract— This paper presents i-AA1 , a constructive, incremental learning algorithm for a special class of weightless, self-organizing networks. In i-AA1 , learning consists of ...
The ability to recognize people is a key element for improving human-robot interaction in service robots. There are many approaches for face recognition; however, these assume unr...
Claudia Cruz, Luis Enrique Sucar, Eduardo F. Moral...
The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a n...