“Lantian Li 15-04-20”版本间的差异
来自cslt Wiki
（以“Weekly Summary 1. Prepare to construct a cohort-based SVM classifier on score-level. 2. Using the "Elbow method" to determine the number of clusters under K-means...”为内容创建页面） |
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| （相同用户的2个中间修订版本未显示） | |||
| 第1行： | 第1行： | ||
Weekly Summary | Weekly Summary | ||
| − | 1. | + | 1. Search for discriminative features for the cohort-based SVM classifier. |
| − | + | We select orignal score, t-norm score, relative ranking, cohort-score, cohort-subtraction-score(delta-score). | |
| − | + | 2. Verify the discriminative ability of the relative ranking and delta-score. | |
| − | 3. Help Pro.Zheng collect | + | Experiments survey that these two features can be applied for SVM classification. |
| + | |||
| + | The best performance is obtained when SVM input feature is made up of orignal score, t-norm score, relative ranking and delta-score. | ||
| + | |||
| + | 3. Help Pro.Zheng collect digit speech. | ||
Next Week | Next Week | ||
| − | 1. Go on the task 1. | + | 1. Go on the task 1 and 2. |
2015年4月20日 (一) 14:35的最后版本
Weekly Summary
1. Search for discriminative features for the cohort-based SVM classifier.
We select orignal score, t-norm score, relative ranking, cohort-score, cohort-subtraction-score(delta-score).
2. Verify the discriminative ability of the relative ranking and delta-score.
Experiments survey that these two features can be applied for SVM classification.
The best performance is obtained when SVM input feature is made up of orignal score, t-norm score, relative ranking and delta-score.
3. Help Pro.Zheng collect digit speech.
Next Week
1. Go on the task 1 and 2.
