PDF《Introducing炉he?》

9 ① The CSE Coding Algorithm ① Find the most confused classes : ② Compute and Extension matrix E which increments ③ Fill h E i d ki i h ③ Fill the empty Extension codes taking into account the confusion of with the rest of the classes. ④ Update Confusion and Separability matrices ④ Update Confusion and Separability matrices. h3 h2 h1 h2 h1 MN * n h5 h4 EN * k h3 + SN * N CN * N

10 0

0 0

2 0

0 4

0 0

6 2

3 3

2 1

2 0

1 1

0 2k * κ > = N

2 5

2 3

0 0

3 0

0 0

0 0

0 0

0 1

0 1

0 1

2 0

2 1

3 2

2 2

1 3 Experiments: Data ? We?tested?the?novel?methodology?on?several?public?datasets? from the UCI Machine Learning Repository from?the?UCI?Machine?Learning?Repository. ? In?addition?we?perform?experiments?with?3?public?Computer? Vision?problems. ? The?ARFace dataset?with?20?classes. ? The?Traffic?Sign?dataset?with?36?classes. ? The MPEG dataset with

70 classes

11 ? The?MPEG?dataset?with?70?classes. Experiments: Methods and Settings p g ?We compare the One vs. All and Dense Random with the CSE coding with ?Classification results are the average over a Stratified

10 fold CV. W th SVM RBF d Ad B t b l ifi ?We use the SVM\RBF and AdaBoost as our base classifier. ?An optimization process is carried out to tune the parameters of the SVMs SVMs. ?SVM\RBF classifiers have

2 parameters to optimize (C & Υ).

12 Experiments and Results ? Results?for?UCI?and?Computer?Vision?experiments?with?SVM? as?the?base?classifier.

13 Experiments and Results ? Results?for?UCI?and?Computer?Vision?experiments?with? AdaBoost?as?the?base?classifier.

14 Conclusions and Future Work ? The Separability Matrix is introduced as a novel tool to The?Separability?Matrix?is?introduced?as?a?novel?tool?to? analyze?and?enhance?ECOC?coding?designs. ? The Extension?Algorithm?proposed?can?be?applied?to?any? existing?ECOC?scheme. ? A?new?coding?design?based?on?the?Separability?matrix?is? introduced obtaining significant performance improvements introduced?obtaining?significant?performance?improvements? over?state\of\the\art?ECOC?designs. ? The?proposed?methodology?reduces?the?number?of?base? p p gy classifiers?needed?in?comparison?with?state\of\the\art?designs. ? A?possible?improvement?will?be?to?optimize?the?initial? Compact?ECOC coding?matrix.

15 Thank you! Thank?you! 16