Josh Torrino,
Perhaps you can try with the software package Libsvm developed by C.J. Lin.You can get it on the web.http://www.csie.ntu.edu.tw/~cjlin/libsvm/
good luck:-)
======= 2005-03-04 01:58:37 您在来信中写道:=======
>Hi, everybody, nice list!
>this my first post, I familiarized myself with SVM method by reading
>tutorials and papers, went through this list and this is my first
>application attempt to solve stochastic time series
>regression/classification problem.
>
>Ok, posing the real problem, would like to get some pointers on 揷an be
>solved or not with SVM?
>
>dynamical stochastic non-linear system goes through 9 probable states.
>states are described by state probability at t, example P9 = 0.67
>i.e. probability that system is in state number 9 is 0.67
>so at any t the system can be described by a 9 element vector = state
>prob. vector
>[P1,P2,P3,...P9] at t
>system is sampled at discrete time intervals and we get discrete time
>series,
>total 10K samples for t:t-10000
>
>next we compute system state trajectory for example past 30 time periods
>using window =30 t, ie lag=30
>thus inputs are 30 columns representing t-1,t-2...t-30,
>i.e. column 1 = lag=1, column2 = lag=2... so on
>9d vector in each column, i.e. per each t representing 9 element state
>prob. vector at t,
>or basically 270 features? ( not sure if 揻eature?in SVM literature is
>same as 搃nput?) in all per sample row
>
>output is same 9 classes as in 9 element vector,
>the goal = try to forecast the next system state, i.e. highest rank
>selection, 9 class classifier
>assume Sum(all 9 state prob) = 1.00
>
>since state probabilities ( inputs ) overlap on density functions,
>historical time trajectories will also overlap
>then output will likely to overlap on pdfs.
>
>note that states are statistically 搖nbalanced? some states are more
>frequent than others, some persist in time
>and some are anti persistent, so classes are going to be unbalanced too.
>
>We抮e basically comparing 10K of 9d system trajectories over past 30 time
>interval window...
>
>the question: what's the chance of successful 9 class separation using SVM
>method as a classifier? :)
>
>and suppose instead of 9 element vector i use 9x3 matrix,
>ie in addition to using Pstate as a single feature i add 2 more features,
>now
>i have 9x3 matrix per t and 9x3x30 = 810 total features per row, again 10K
>rows.
>what are my chances then, "good", "bad" or "forget it"? :)
>
>so I guess, to solve i need an SVM software that can handle:
>- large data set > 10K
>- large input feature set, > 200 but < 1000
>- multi class > 10 classes, with ranking or win-takes-all type
>classification
>- unbalanced classes, cost handling
>- and preferably at least grid search to find opt. parameters.
>
>is there SVM software that can handle all of that, matlab or C or
>ported to windows? With so many svm toolboxes and code I am kind of lost
>as far as which one I should try?it抣l take a while to get through them
>all.
>
>Maybe there is an alternative ML method that抯 more appropriate?
>
>Opinions, pointers?
>
>Thank you kindly.
>Josh.
>p.s. this is basically a typical physics problem, i.e. we get a N state
>system
>with state prob. matrix size Nx1, we compute it's trajectory over a window,
>try to forecast the next system state... and adding other system
>descriptors besides
>state probabilities...( try to add more features for better separation ? )
= = = = = = = = = = = = = = = = = = = =
致
礼!
J. T. Huang
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2005-03-04
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