Research map / Learning theory and algorithms / Prediction and decision losses
Sparse Perceptron Decision Tree for Millions of Dimensions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn sparse hyperplane per node with budget constraint (SPDT) instead of dense linear classifier (l2-SVM) | SPDT · epsilon | test error rate (%): 13.24 -> 12.45(8) | Helped |
| SPDT vs LDKL nonlinear local kernel baseline | SPDT · colon | test error rate (%): 16.67 -> 13.33(1) | Helped |
| use tree structure with feature selection (SPDT) instead of flat sparse classifier FGM | SPDT · news20 (1,000 features) | test error rate (%): 9.40 -> 2.45 | Helped |
| budgeted sparse perceptron nodes instead of GBFS | SPDT · rcv training time (900 features) | training time (s): 8523.3s -> 41.9s | Helped |
| SPDT on rcv vs l2-SVM | SPDT · rcv | test error rate (%): 4.05 -> 3.85(5) | No clear effect |
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