Comprehensive sensory learning is the treatment of theories and techniques for computer systems to automatically learn to understand comprehensive visual, auditory and other sensory information with minimal human-imposed rules about the world. The concept of comprehensive learning here implies two coverages: comprehensive coverage of the sensory world and comprehensive coverage of the recognition algorithm. The SHOSLIF is a framework aiming to provide a unified theory and methodology for comprehensive sensor-actuator learning. Its objective is not just to attack a particular sensory or actuator problem, but a variety of such problems. It addresses critical problems such as how to automatically select the most useful features; how to automatically organize sensory and control information using a coarse-to-fine space partition tree which results in a very low, logarithmic time complexity for content-based retrieving from a large visual knowledge base, how to handle invariance based on learning, how to enable on-line incremental learning, how to conduct autonomous learning etc.
SHOSLIF in
AAAI97 robot
exhibition.
J. Weng, ``Cresceptron and SHOSLIF: Toward Comprehensive Visual Learning,''
in S. K. Nayar and T. Poggio (eds.),
Early Visual Learning, Oxford University Press, New York,
pp. 183 - 214, 1996.
Click
here
to down load the paper (PostScript).
J. Weng, ``SHOSLIF: A framework for sensor-based learning
for high-dimensional complex systems,''
invited paper in Proc. IEEE Workshop
on
Architectures for Semiotic Modeling
and situation analysis
in Large Complex Systems, Monterey, CA, Aug. 27-29, 1995.
Click
here
to down load the paper (PostScript).
J. Weng, ``On comprehensive visual learning,'' invited paper
in Proc.
NSF/ARPA Workshop on Performance vs. Methodology in Computer Vision,
Seattle, WA, pp. 152-166, June 24-25, 1994.
Click
here
to down load the paper (PostScript).
J. Weng, ``SHOSLIF: A framework for object recognition from images,''
invited paper in
Proc. IEEE International Conference on Neural Networks,
Orlando, FL, pp. 4204-4209, June 28 - July 2, 1994.
J. Weng, ``SHOSLIF: A Learning System for Vision and Control,''
invited paper in Proc. IEEE Annual Workshop on
Architectures for Intelligent Control Systems,
Columbus, Ohio, August 16, 1994.
To Weng's Home Page: http://web.cps.msu.edu/~weng/