Those are learned questions.
Clearly, you are not a novice at this.
My newest AI could best be described as a Deep Learning, Topological Data Analysis, Genetic Algorithm.
The engine is modeled after the LS-1 engine, which ultimately became known as the Pittsburgh Model.
The
topological system is all about segmenting the handicapping
scenarios.
It has a multi-step process, which includes:
1) Intelligent Data collection
Emphasis on Data shapes - to aid with a convolutional approach.
IOW, the engine builds its own factor combinations.
It also learns via a "top-to-bottom" approach, much as a convolutional DL operation would.
Think: Before you can push to find the most profitable, highest Opt%, etc. the engine must figure out:
* Who the public will likely bet
* Who is likely to go to the front
* Who is likely to have the high probabilities
etc. etc.
Hence, top-to-bottom.
(Technically, I think of it as left-to-right because I visualize the layers as being vertical.)
2) Classification
Which of the hundreds of system components should be used in this race and/or with this horse?
3) Segmentation
This is the topological component. It uses the Classification to decide which segmented system(s) are appropriate for this
situation.
4) Handicapping Analysis
Ultimately, it comes down to:
A. What are the probabilities.
B. What should the odds be?
5) Betting Analysis
This is steps 1 thru 4, applied to what happens
AFTER the race is handicapped. (Logically, these would be steps 5.1 thru 5.4.)
Right now, I am in the middle of coding step 5.
Dave
