最新的支持向量機工具箱，有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
標簽：
支持向量機
工具箱
上傳時間：
2013-12-16
上傳用戶：亞亞娟娟123
prolog 找路例子程序：
=== === === === === ===
Part 1-Adding connections
Part 2-Simple Path
example
| ?- path1(a,b,P,T).
will produce the response:
T = 15
P = [a,b] ?
Part 3 - Non-repeating path
As an example, the query:
?- path2(a,h,P,T).
will succeed and may produce the bindings:
P = [a,depot,b,d,e,f,h]
T = 155
Part 4 - Generating a path below a cost threshold
As an example, the query:
?- path_below_cost(a,[a,b,c,d,e,f,g,h],RS,300).
returns:
RS = [a,b,depot,c,d,e,g,f,h] ?
RS = [a,c,depot,b,d,e,g,f,h] ?
no
==================================
標簽：
Part
connections
example
prolog
上傳時間：
2015-04-24
上傳用戶：ljt101007
