Neural Networks & SVMs - Horse Racing Forum - PaceAdvantage.Com

BHURRICANE · 07-25-2005, 05:36 AM

Would anyone care to share any success or failures implimenting neural networks, support vector machines, kernel machines, etc. to assist in the handicapping process? Specifically, I have been exploring various encoding and reduction schemes to the race data while at the same time preserving the intrinsic representations in lower dimensions. Any advice or experiences would be helpful. Thanks.

hurrikane · 07-25-2005, 09:20 AM

Hey,
what's up with the name dude?

hurrikane · 07-25-2005, 09:22 AM

I am no NN guru but my experience is that there is a lot of noise and redundancy in the data. Not great results. do you intend on using drf data?

But I am still working on it.

ceejay · 07-25-2005, 10:11 AM

You might try googling "neural network horse." There was a guy that used to hang out here who had a NN program for sale, but I haven't seen him for a while....

DJofSD · 07-25-2005, 10:41 AM

I started a project many years ago involving NN. I never got very far. Beyond the initial purchase of a few books and workbooks, I could not figure out if the various pre packaged NN engines would work for me. And they're too expensive to try too many of them doing a trail and error.

This was many years ago. I'm sure things have changed in the meantime.

I'd be interested in learning anything more about NN and handicapping.

DJofSD

Grifter · 07-25-2005, 01:11 PM

OK, I'm impressed. I had to look up SVM.... read all about it. Still haven't the foggiest....

I also fooled around with NN. Frankly, it seems too much "black-box" for my tastes. I cannot get over the fear of "over-fitting".

I think I trust multiple regression analysis, in all its modes, to NN. (I'd lump SVM with NN as well, if I thought I knew what I was talking about.)

-- Grifter

kenwoodallpromos · 07-25-2005, 01:45 PM

I want to know how he got a "dstinguished" remark after 1 post!

ceejay · 07-25-2005, 02:08 PM

Quote:

Originally Posted by Grifter

I think I trust multiple regression analysis, in all its modes, to NN.

In my experience (not racing) MLA regression works as well as NN in most cases: I have the same fears as you about over-fitting. I have had some (but rare) situations where "fuzzy logic" (which is similar to NN) has worked but I view it, and NN as a "last resort."

Fuzzy logic links:
http://www.oilfield.slb.com/media/se..._techsheet.pdf
http://www-2.cs.cmu.edu/Groups/AI/ht...faq-doc-2.html

Dave Schwartz · 07-25-2005, 04:29 PM

BHurrikane,

Not sure what you are looking for here but I can tell you my experience with neural net technology has both strong points and weak points. As for vector machines, this is a new approach and I doubt there will be much if anything out there as it applies to horse racing.

Neural nets are good at making "generalizations," and, as such, are theoretically a good match for horse races. The problem is that they have a built in sensitivity to "bad data." To that end, "bad data" (however you choose to define it) must be culled from the data set. IMHO, this makes neural nets a poor choice for horse racing.

A better choice is the genetic algorithm approach.

Where are you in this process? Are you looking to develop something for yourself or purchase something already developed? Are you a software engineer or a hobbiest?

There are some very astute people here and if you give them a better question, I think you will receive some meaningful answers.

Hmm... The above package makes me ask, "Where is GameTheory these days?"

Regards,
Dave Schwartz

BHURRICANE · 07-25-2005, 06:58 PM

Quote:

Originally Posted by hurrikane

I am no NN guru but my experience is that there is a lot of noise and redundancy in the data. Not great results. do you intend on using drf data?

But I am still working on it.

This is exactly why I'm interested in using a neural network. In the little exploring I've done on the internet, I've found that the neural network implimentations to date are very unsophisicated with their approach in "presenting" the data to the neural network. There are almost no provisions for for standardization of the data as well as the removal of "outlier" or errand data that would effect the performance. I think I have those situations accounted for.

BHURRICANE · 07-25-2005, 07:03 PM

Quote:

Originally Posted by Grifter

OK, I'm impressed. I had to look up SVM.... read all about it. Still haven't the foggiest....

I also fooled around with NN. Frankly, it seems too much "black-box" for my tastes. I cannot get over the fear of "over-fitting".

I think I trust multiple regression analysis, in all its modes, to NN. (I'd lump SVM with NN as well, if I thought I knew what I was talking about.)

-- Grifter

Over-fitting can be a problem. This can be avoided with a properly designed neural network however. If the learning rate and momentum is adjusted in concert with monitoring the rate at which the absolute error is decending, you can avoid over-fitting (or over-training) a network.

Multiple regression analysis is useful for a single horse evaluation, however, seems to lose some utility when applied to a race as a whole for me. I'm interested in pattern identification as well as the classification aspects that a neural network can provide.

BHURRICANE · 07-25-2005, 07:14 PM

Quote:

Originally Posted by Dave Schwartz

BHurrikane,

Not sure what you are looking for here but I can tell you my experience with neural net technology has both strong points and weak points. As for vector machines, this is a new approach and I doubt there will be much if anything out there as it applies to horse racing.

Neural nets are good at making "generalizations," and, as such, are theoretically a good match for horse races. The problem is that they have a built in sensitivity to "bad data." To that end, "bad data" (however you choose to define it) must be culled from the data set. IMHO, this makes neural nets a poor choice for horse racing.

A better choice is the genetic algorithm approach.

Where are you in this process? Are you looking to develop something for yourself or purchase something already developed? Are you a software engineer or a hobbiest?

There are some very astute people here and if you give them a better question, I think you will receive some meaningful answers.

Hmm... The above package makes me ask, "Where is GameTheory these days?"

Regards,
Dave Schwartz

I have been using a hybrid GA / Neural Net and had large sucess with it.

http://www.cs.utexas.edu/users/kstanley/neat.html

The NEAT model allows for the idea of "competition" among entities with
the GA approach and the neural side is used as a learning utility. I think
this is fantastic work that might revitalize some interest in neural networks
as a tool to facilitate a "learning" system. Simple back-prop isn't enough alone.

Your correct in that the trash data must be removed. Right now my software
tosses the "exceptional" data values away.

Also, I have been using a little ID3, for decision tree style approaches to
consider what data to put into the system. This acts alot like a person
does to pre-filter the data under consideration.

I am a software engineer by trade, and I have a hobbiest interest in horseracing.

BHURRICANE · 07-25-2005, 07:26 PM

Quote:

Originally Posted by Grifter

OK, I'm impressed. I had to look up SVM.... read all about it. Still haven't the foggiest....

I also fooled around with NN. Frankly, it seems too much "black-box" for my tastes. I cannot get over the fear of "over-fitting".

I think I trust multiple regression analysis, in all its modes, to NN. (I'd lump SVM with NN as well, if I thought I knew what I was talking about.)

-- Grifter

I do want to comment here that SVM's are much more effective in their output as it relates to horse racing than neural networks in my experiments so far. They aren't subject to some of the tainting that can occur with back-prop networks. Hybrid approaches to kernel style computing is proving quite an effective means to achieve more human quality decision making. SVM is a great approach to integrating regression based systems.

Dave Schwartz · 07-25-2005, 08:36 PM

BH,

Quote:

I have been using a hybrid GA / Neural Net and had large sucess with it.

http://www.cs.utexas.edu/users/kstanley/neat.html

The NEAT model allows for the idea of "competition" among entities with
the GA approach and the neural side is used as a learning utility. I think
this is fantastic work that might revitalize some interest in neural networks
as a tool to facilitate a "learning" system. Simple back-prop isn't enough alone.

Your correct in that the trash data must be removed. Right now my software
tosses the "exceptional" data values away.

Most interesting. My personal approach is towards genetic "agents" that are (somewhat) free to roam the database building rules and assigning points as they see fit.

When I built my first "competitor" neural net in ThoroBrain2, c1990 (a couple of years before the BrainMaker guy went public with it), I found that it was highly predictive. But there was always the problem of culling the bad races.

And the tough part of that is that not all bad races are obviously so. I mean, the race where the 4 horse falls down and takes the 3 inside horses with him is obvious.

The problem is the 4/5, wire-to-wire monster who never comes out of the gate. Is that an abberation or is it predictable within the data?

If it is not predictable within the data, then the race should be removed.

The problem is that this is a pretty regular occurence, happening several times per card. (Maybe not with this precise example, but I am sure you get the idea.)

The important part of your quote is:

Quote:

I have been using a hybrid GA / Neural Net and had large sucess with it.

That says a lot.

I wish you continued success.

Regards,
Dave Schwartz

BHURRICANE · 07-25-2005, 11:19 PM

Quote:

Originally Posted by Dave Schwartz

BH,

Most interesting. My personal approach is towards genetic "agents" that are (somewhat) free to roam the database building rules and assigning points as they see fit.

When I built my first "competitor" neural net in ThoroBrain2, c1990 (a couple of years before the BrainMaker guy went public with it), I found that it was highly predictive. But there was always the problem of culling the bad races.

And the tough part of that is that not all bad races are obviously so. I mean, the race where the 4 horse falls down and takes the 3 inside horses with him is obvious.

The problem is the 4/5, wire-to-wire monster who never comes out of the gate. Is that an abberation or is it predictable within the data?

If it is not predictable within the data, then the race should be removed.

The problem is that this is a pretty regular occurence, happening several times per card. (Maybe not with this precise example, but I am sure you get the idea.)

The important part of your quote is:

That says a lot.

I wish you continued success.

Regards,
Dave Schwartz

I agree. I remove any race that doesn't "feel" right. We cannot plan or predict strange occurances. I simply omit the race and move on. I'm focused on how the race would run under ideal circumstances and conditions, and then hope that the race resembles those to a certain degree, and subsequently the results are somewhat more predictable in addition to assistance in identifying overlays etc. Systems are programmed and based on rules not exceptions, humans are exception handlers. If I see something I think my program "missed" then I back test it to determine that or find out if I'm seeing something that doesn't "normally" occur.