| 摘要 |
Methods and systems are disclosed for autonomously building a predictive model of outcomes. A most-predictive set of signals Sk is identified out of a set of signals s1, s2,..., S D for each of one or more outcomes o k . A set of probabilistic predictive models Ô k = M k (S k ) is autonomously learned, where Ô k is a prediction of outcome o k derived from the model M k that uses as inputs values obtained from the set of signals S k . The step of autonomously learning is repeated incrementally from data that contains examples of values of signals s 1 , s 2 ,..., s D and corresponding outcomes o 1, o 2,..., o K . Various embodiments are also disclosed that apply predictive models to various physiological events and to autonomous robotic navigation. |
