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Presentation- Week 9

Presentation- Week 9. Maya Shoham. Baselines. Eth80 Database 30 Train, 50 Test 70.25% Accuracy The paper that used the eth80 database used all 400 pictures for training + test and got a best classification rate of 83%. Caltech101 Database 30 train, 1-50 test

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Presentation- Week 9

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  1. Presentation- Week 9 Maya Shoham

  2. Baselines • Eth80 Database • 30 Train, 50 Test • 70.25% Accuracy • The paper that used the eth80 database used all 400 pictures for training + test and got a best classification rate of 83%. • Caltech101 Database • 30 train, 1-50 test • Lambda=20, Iterations=5000, 20% accuracy • Possibly not optimal lambda, but code takes about 9 hrs to run so its hard to check multiple lambdas.

  3. Other Changes • Split up the feature vectors into training and testing before the kernel matrix is generated. • Allows for greater flexibility in generating different kernel matrices. • Sped up the softmax logistic regression

  4. Training the Level Weights • Feature vectors are 4200 x number of images. • 4200 corresponds to the 21 bins x 200 clusters. • Kernel Matrix to train the level weights is a 4200 x 4200.

  5. Whats Next? • Should the level weights be weighted by feature? (4200 weights) or by bin? (21 weights) • How do we label the training kernel for the learning of the level weights? • Write code to convert from the 4200x4200 kernel matrix to the #images x #images kernel matrix so that we can alternately weight the levels weights and the other weights.

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