Plot multi-class decision boundaries SVM?

Question

AR 2016-4-26

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/281172-plot-multi-class-decision-boundaries-svm

Does anyone know how to plot Plot multi-class decision boundaries for SVM?

I'm doing Handwritten Digit classification so have 10-classes w/256-predictors and using "fitcecoc" and "predict" but having problems plotting the mixed-model, decision boundaries.

Any suggestions?

predictorExtractionFcn = @(x) array2table(x, 'VariableNames', predictorNames); svmPredictFcn = @(x) predict(classificationSVM, x); trainedClassifier.predictFcn = @(x) svmPredictFcn(predictorExtractionFcn(x));

predictorNames = {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96', 'column_97', 'column_98', 'column_99', 'column_100', 'column_101', 'column_102', 'column_103', 'column_104', 'column_105', 'column_106', 'column_107', 'column_108', 'column_109', 'column_110', 'column_111', 'column_112', 'column_113', 'column_114', 'column_115', 'column_116', 'column_117', 'column_118', 'column_119', 'column_120', 'column_121', 'column_122', 'column_123', 'column_124', 'column_125', 'column_126', 'column_127', 'column_128', 'column_129', 'column_130', 'column_131', 'column_132', 'column_133', 'column_134', 'column_135', 'column_136', 'column_137', 'column_138', 'column_139', 'column_140', 'column_141', 'column_142', 'column_143', 'column_144', 'column_145', 'column_146', 'column_147', 'column_148', 'column_149', 'column_150', 'column_151', 'column_152', 'column_153', 'column_154', 'column_155', 'column_156', 'column_157', 'column_158', 'column_159', 'column_160', 'column_161', 'column_162', 'column_163', 'column_164', 'column_165', 'column_166', 'column_167', 'column_168', 'column_169', 'column_170', 'column_171', 'column_172', 'column_173', 'column_174', 'column_175', 'column_176', 'column_177', 'column_178', 'column_179', 'column_180', 'column_181', 'column_182', 'column_183', 'column_184', 'column_185', 'column_186', 'column_187', 'column_188', 'column_189', 'column_190', 'column_191', 'column_192', 'column_193', 'column_194', 'column_195', 'column_196', 'column_197', 'column_198', 'column_199', 'column_200', 'column_201', 'column_202', 'column_203', 'column_204', 'column_205', 'column_206', 'column_207', 'column_208', 'column_209', 'column_210', 'column_211', 'column_212', 'column_213', 'column_214', 'column_215', 'column_216', 'column_217', 'column_218', 'column_219', 'column_220', 'column_221', 'column_222', 'column_223', 'column_224', 'column_225', 'column_226', 'column_227', 'column_228', 'column_229', 'column_230', 'column_231', 'column_232', 'column_233', 'column_234', 'column_235', 'column_236', 'column_237', 'column_238', 'column_239', 'column_240', 'column_241', 'column_242', 'column_243', 'column_244', 'column_245', 'column_246', 'column_247', 'column_248', 'column_249', 'column_250', 'column_251', 'column_252', 'column_253', 'column_254', 'column_255', 'column_256'}; predictors = inputTable(:, predictorNames); response = inputTable.column_257;