Objective Function Analysis:用于机器学习的神经网络的替代方案。-开源
需积分: 9 112 浏览量
2021-05-31
19:51:48
上传
评论
收藏 693KB DOCX 举报
Objective Function Analysis
An Alternative to Neural Net Machine Learning
Phil Gillaspy, Ph.D.
July, 2019
Objective Function Analysis models intelligence as a multi-dimensional probability mass
function (MD-PMF) of the perceptions and responses (which are themselves perceived)
of an entity and it’s inherent objective function (OF). We say that the entity has
intelligence because it maintains the joint probability
P[OF, r
j
, p
i
] (1)
where
OF is the objective function,
p
i
is 1...n perceptions of the entity,
r
j
is 1...m responses of the entity (note that responses are perceived just as if they
were perceptions).
The OF expresses the probability of a successful response to the given set of
perceptions. The learning algorithm is the action of
1) For the jth response, project and evaluate the effectiveness of the response by
calculating the objective function (OF) for the jth response subject to the given set of
perceptions.
2) Update the conditional probability of the OF corresponding to the jth response,
P[OF | r
j
, p
i
] (2)
3) Repeat steps 1 and 2 for all possible responses, then proceed to step 4.
4) Find the maximum conditional expected OF,
max { <OF>
j
= ∑
k
OF
k
* P[OF | r
j
, p
i
] }, (3)
then the actual response of the entity is the response corresponding to max{<OF>
j
}.
5) Having responded as in step 4, the entity is placed into a new set of values for the
perceptions. The entity continues responding per steps 1 through 4 above.
剩余28页未读，继续阅读
评论0