Hierarchical Classifier - Notable Researchers

Notable Researchers

Tomaso Poggio of MIT does research in the area of computer vision and has recently been developing a hierarchical vision system that is both relatively simple and empirically comparable to the human brain. The research combines neural networks with lots of cognitive psychology and neuroscience data in an attempt to create the most realistic and human-like artificial vision system that exists.

Tom Dean of Brown University researches graphical models and Bayesian networks and is currently developing a hierarchical system that claims to have very good results in vision problems. This model is able to very simply produce properties such as rotational and translational invariance that a reliable vision system needs in order to yield non-trivial results.

Jeff Hawkins is the founder of Palm Computing and the Redwood Neuroscience Institute and is the author of On Intelligence in which he proposes his theories on the workings of the brain that center around hierarchical processing and the brain as a generic pattern machine that functions by continually abstracting and categorizing data.

Leslie Lamport is the author of the paper "How to write a proof", in which he proposes to write proofs in a hierarchical fashion with main ideas, sub-ideas, sub-sub-ideas, etc. The proofs are written in such a way that they mirror the structure of a tree. Some automated theorem provers of today have attempted to capitalize on this formalization of the structure of proofs as to more efficiently solve problems. However, none of these theorem provers have the capabilities to adequately solve problems across domains as current vision systems are beginning to be able to do. Thus, it is highly possible but still unknown whether similar tactics to the ones used in the vision system and specifically hierarchical processing can dramatically improve automated theorem provers.