Learning to learn: From smart machines to intelligent machines

Authors:
B. Raducanu;J. Vitrií
Affiliations:
Computer Vision Center, Edifici "O" - Campus UAB, 08193 Bellaterra, Barcelona, Spain;Computer Vision Center, Edifici "O" - Campus UAB, 08193 Bellaterra, Barcelona, Spain and Computer Science Department, Autonomous University of Barcelona, 08193 Bellaterra, Barcelona, Spain
Venue:
Pattern Recognition Letters
Year:
2008

Citing 7
Cited 1

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Building Brains for Bodies

Building Brains for Bodies
Context-based vision system for place and object recognition

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection

International Journal of Computer Vision
Context in problem solving: a survey

The Knowledge Engineering Review
Architectures for context

Human-Computer Interaction
Chunk incremental LDA computing on data streams

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II

Recognizing occluded faces by exploiting psychophysically inspired similarity maps

Pattern Recognition Letters

Quantified Score

Hi-index	0.11

Visualization

Abstract

Since its birth, more than five decades ago, one of the biggest challenges of artificial intelligence remained the building of intelligent machines. Despite amazing advancements, we are still far from having machines that reach human intelligence level. The current paper tries to offer a possible explanation of this situation. For this purpose, we make a review of different learning strategies and context types that are involved in the learning process. We also present the results of a study on cognitive development applied to the problem of face recognition for social robotics.