Machine learning for access and retrieval II
author:
Jia Li,
Pennsylvania State University
Categories
Top: Computer Science: Machine Learning: Markov ProcessesTop: Computer Science: Machine Learning: Clustering
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| Slides | |
| 0:01 | Hidden Markov Models with Applications |
| 22:31 | Outline |
| 24:36 | Clustering by Mixture Models |
| 24:45 | Clustering |
| 25:51 | Clustering |
| 26:31 | Approaches to Clustering |
| 28:01 | Mixture Model-based Clustering |
| 28:40 | Clustering |
| 30:34 | Mixture Model-based Clustering |
| 31:19 | Mixture of Normals |
| 33:35 | Mixture Model-based Clustering |
| 36:11 | Advantages |
| 37:48 | EM Algorithm |
| 38:30 | Mixture of Normals |
| 39:45 | EM Algorithm |
| 44:48 | EM Algorithm |
| 46:15 | EM Algorithm |
| 47:19 | EM for the Mixture of Normals |
| 48:51 | EM for the Mixture of Normals |
| 49:17 | EM for the Mixture of Normals |
| 49:57 | EM for the Mixture of Normals |
| 50:46 | EM for the Mixture of Normals |
| 51:54 | EM for the Mixture of Normals |
| 52:42 | EM for the Mixture of Normals |
| 54:04 | Computation Issues |
| 57:22 | Examples |
| 58:21 | Examples |
| 59:51 | Classification Likelihood |
| 60:42 | Classification EM |
| 60:54 | Classification EM |
| 61:27 | Outline |
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