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00089 runtime in cpu seconds 060 number of sv 141

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Unformatted text preview: 65 examples read) Setting default regularization parameter C=0.0000 Optimizing...<snip>...done. (163 iterations) Optimization finished (9 misclassified, maxdiff=0.00089). Runtime in cpu-seconds: 0.60 Number of SV: 141 (including 77 at upper bound) L1 loss: loss=35.80801 Norm of weight vector: |w|=0.00365 Norm of longest example vector: |x|=4380.65657 Estimated VCdim of classifier: VCdim<=142.17389 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.01 XiAlpha-estimate of the error: error<=0.92% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>99.12% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>99.15% (rho=1.00,depth=0) Number of kernel evaluations: 135983 Writing model file...done Cite as: Tommi Jaakkola, course materials for 6.867 Machine Learning, Fall 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. In this case, nine training examples are misclassified. Again, there are many ways of extracting the misclassified images. Here is how it is done with unix power tools: $ svm_classify train-01-images.svm svm_model Reading model...OK. (141 support vectors read) Classifying test examples..<snip>..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 99.93% (12656 correct, 9 incorrect, 12665 total) Precision/recall on test set: 99.93%/99.94% $ cat train-01-images.svm | awk ’{ print $1 }’ | paste - svm_predictions \ | nl | awk ’{ if ($2 * $3 < 0) { print $1 } }’ 315 2824 4496 4612 6031 6627 8185 8547 9327 Here are the images: train-0003...
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