Related Experiment Videos
The optimal crowd learning machine
Bilguunzaya Battogtokh1, Majid Mojirsheibani2, James Malley1
1Center for Information Technology, National Institutes of Health, Bethesda, MD USA.
Background:
Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness.
Results:
For making predictions on an outcome, it is provably at least as good as the best machine in the family, given sufficient data. And if one machine in the family minimizes the probability of misclassification, in the limit of large data, then Optimal Crowd does also. That is, the Optimal Crowd is asymptotically Bayes optimal if any machine in the crowd is such.
Conclusions:
The only assumption needed for proving optimality is that the outcome variable is bounded. The scheme is illustrated using real-world data from the UCI machine learning site, and possible extensions are proposed.
Related Concept Videos
Machines: Problem Solving II
Machines: Problem Solving I
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Introduction to Learning
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Cognitive Learning
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Machines
A free-body diagram of the...