NewsGroup (table for single real feature)
| Condition\Algorithm |
SparseAveragedPerceptron |
SparseWinnow |
PassiveAggresive |
SparseConfidenceWeighted |
BinaryMIRA |
| 1 round w/o real features |
48.916 |
92.597 |
19.038 |
|
33.739 |
| 1 round w/ real features |
47.753 |
92.491 |
23.268 |
|
32.364 |
| 10 rounds w/o real features |
82.390 |
91.539 |
24.802 |
|
76.891 |
| 10 rounds w/ real features |
82.126 |
91.529 |
12.427 |
|
75.939 |
| 50 rounds w/o real features |
84.823 |
91.592 |
14.120 |
|
77.208 |
| 50 rounds w/ real features |
85.299 |
91.433 |
19.566 |
|
76.891 |
| 100 rounds w/o real features |
85.828 |
91.433 |
12.956 |
|
76.574 |
| 100 rounds w real features |
84.770 |
91.486 |
15.442 |
|
61.026 |
NewsGroup (table for the same amount of Gaussian random real features as discrete ones)
| Condition\Algorithm |
SparseAveragedPerceptron |
SparseWinnow |
PassiveAggresive |
BinaryMIRA |
| 1 round w/o real features |
51.454 |
92.597 |
12.057 |
33.739 |
| 1 round w/ real features |
17.980 |
6.081 |
14.913 |
14.225 |
| 10 rounds w/o real features |
82.813 |
91.539 |
22.369 |
76.891 |
| 10 rounds w/ real features |
52.829 |
|
42.517 |
45.743 |
| 50 rounds w/o real features |
84.294 |
91.592 |
21.100 |
77.208 |
| 50 rounds w/ real features |
75.727 |
|
67.054 |
75.198 |
| 100 rounds w/o real features |
85.506 |
91.433 |
17.768 |
76.574 |
| 100 rounds w real features |
77.631 |
|
74.828 |
74.194 |
Badges (table for single real feature)
| Condition\Algorithm |
SparsePerceptron |
SparseWinnow |
NaiveBayes |
| 1 round w/o real features |
100.0 |
95.745 |
100.0 |
| 1 round w/ real features |
100.0 |
95.745 |
100.0 |
| 10 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 10 rounds w/ real features |
100.0 |
100.0 |
100.0 |
| 50 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 50 rounds w/ real features |
100.0 |
100.0 |
100.0 |
| 100 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 100 rounds w real features |
100.0 |
100.0 |
100.0 |
Badges (table for same amount of constant real features as discrete features)
| Condition\Algorithm |
SparsePerceptron |
SparseWinnow |
NaiveBayes |
| 1 round w/o real features |
100.0 |
95.745 |
100.0 |
| 1 round w/ real features |
74.468 |
100.0 |
100.0 |
| 10 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 10 rounds w/ real features |
78.723 |
100.0 |
100.0 |
| 50 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 50 rounds w/ real features |
100.0 |
100.0 |
100.0 |
| 100 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 100 rounds w real features |
100.0 |
100.0 |
100.0 |
Badges (table for same amount of of random Gaussian real features as discrete features)
| Condition\Algorithm |
SparsePerceptron |
SparseWinnow |
NaiveBayes |
| 1 round w/o real features |
100.0 |
95.745 |
100.0 |
| 1 round w/ real features |
55.319 |
56.383 |
100.0 |
| 10 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 10 rounds w/ real features |
62.766 |
100.0 |
100.0 |
| 50 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 50 rounds w/ real features |
74.468 |
87.234 |
100.0 |
| 100 rounds w/o real features |
100.0 |
100.0 |
100.0 |
| 100 rounds w real features |
86.170 |
100.0 |
100.0 |
NewsGroup (table for single real feature)
NewsGroup (table for the same amount of Gaussian random real features as discrete ones)
Badges (table for single real feature)
Badges (table for same amount of constant real features as discrete features)
Badges (table for same amount of of random Gaussian real features as discrete features)