Weka Smo Linear Kernel Correction Notes

 

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In some cases, your computer may generate an error code indicating that the linear kernel is weka smo. There are many reasons that can cause this problem.

 

 

Implements John Platt’s sequential minimum optimization algorithm for the coaching support vector classifier.

This worldwide implementation replaces any missing values ​​and converts tiny attributes to binary. In fact it will normalize all the attributes by default. (In this pocket, the coefficients are in the output for normalized data, not normal data – this is important for the interpretation of the classifier.)

Multiclass Problems are solved using pairwise classification (also known as 1-vs-1).

To obtain correct estimates of the likelihood, use a selection that matches the calibration models for the vector support engine products. In the case of multiple classes, the predicted probabilities are blended using the directional pairwise Hasti and Tibshirani method.

Note. To improve speed, you may need to turn off normalization when using SparseInstances.

J. Platt: Fast Learning of Sequential Support Vector Machines with Minimal Optimization. Written by B. Shelkopf, K. Burgess and A. Smo а, editors, Advances Kernel in Methods – Support Vector Learning, 1998.

S.S. Kirti, S.K. Shevade, C. Bhattacharya, K.R.K. Murthy (2001). Improvements in Platt’s SMO for the development of the SVM classifier algorithm. Neural computing. 13 (3): 637-649.

Trevor Hasti, Robert Tibshirani: By Classifying Pairwise Communication, In: Advances in Neural Information Processing Systems, 1998.

@ incollectionPlatt1998, Author = J. Platt, booktitle = advanced kernel methods – vector learning support, Appendix = B. Shelkopf and K. Burgess A. Resin, MIT Press participates in the publishing house, Short Topic = Training Support Vector Machines to Use Sequential Minimum Optimization. Year of construction = 1998, URL stands for http://research.microsoft.com/~jplatt/smo. html, PS stands for http://research.microsoft.com/~jplatt/smo-book.ps.gz, PDF = http://research.microsoft.com/~jplatt/smo-book.pdf @ articleKeerthi2001, The author is S.S. Kirti, S.K. Shevade, C. Bhattacharya and K.R.K. Murti, Article = neural computing, Assortment = 3, Return = 637-649, The name implies improvements to Platt’s SMO algorithm to develop an SVM classifier, The volume is 13, Year means 2001, = ps http://guppy.mpe.nus.edu.sg/~mpessk/svm/smo_mod_nc.ps.gz@ inproceedingsHastie1998, The author is incredibly similar to Trevor Hasti and Robert Tibshirani, booktitle = In advanced neural information processing systems, Editor corresponds to Michael E. Jordan, Michael J. Kearns and Sarah A. Solla. Manager = press, with 7 steps = classification by pairing, Volume = 10, Year = 1998, PS = http://www-stat.stanford.edu/~hastie/Papers/2class.ps

 -no check  Turns off all monitors - use with caution!  Their inclusion assumes that the data is purely digital and not  contains all missing values ​​and has a nominal class. Turn  also usually means that no header information should be written when  The machine could be linear. Ultimately, it is also assumed that a small instance  The weight to help you is 0.  (Default: Enabled) 

 -C  Complexity constant C. 1) 

(Initially -N If 0 = normalize / 1 = normalize / 2 = no. (Standard 0 = Normalize)

 -L   Tolerance parameter. (Standard 1. 0e-3) 

 -P   Epsilon for rounding errors. (Standard 1.0e-12) 

-M Correction of SVM model outputs calibration.

  -v The number of folds for our inner  Cross validation. (Default -1, use training data) 

  -w Seed of the random number. (Standard 1) 

-K Use the kernel positively. (Standard: weka.classifiers.functions.supportVector.PolyKernel)

 calibrator  The fully qualified name of the normalization model, followed by parameters.  (Standard: "weka.classifiers.functions.Logistic") 

 output debug information  If set, the classifier will run normally in debug mode and  may display more information about this console 

 -not-check-skills  If set, the capacity of the classifier is not checked before verifying that the classifier is to be built.  (Use with care). 

 - decimal place The number of decimal places to display numbers in the entire model (default 2). 

 Kernel parameters weka.classifiers.functions.supportVector.PolyKernel: 

-e The exponent to use. (Default: 1.0)

 -l Use lower-order terms.  (Default: none) 

-C Cache size (prime), only 0 for cache and -1 so you can turn it off. (Standard: 250007)

 output debug information  Prints debug information (if any). (Default: Disabled) 

 -no check  Disables multiple tests - use with caution! (Default: search) 

Parameters for the weka.classifiers.functions.Logistic calibrator: -C Use conjugate gradient descent rather than BFGS updates.

 -R   Fit the peak to the probability of the logarithm. 

-M Determines the largest number of iterations (default -1, correct convergence).

 output debug information If set, the classifier will run normally in debug mode and  will probably provide more information about this console 

 -not-check-skills If set, the classifier parameters are not checked until the classifier is definitely created.  (Use with care). 

 - decimal place I would say howdecimal places is of interest for displaying numbers in the model (default 2). 

 

 

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