图 书 馆
音乐流派分类的线性规划推进Linear Programming Boosting for Classification of Musical Genre |
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| 课程网址: | http://videolectures.net/mbc07_diethe_lpb/ |
| 主讲教师: | Tom Diethe |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2007-12-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Classification of musical genre from raw audio files is a fairly well researched area of music research, and as such provides a good starting point for testing a new algorithm. The Music Information Retrieval Evaluation eXchange (MIREX) is a yearly competition in a wide range of machine learning applications in music. MIREX 2005 included a genre classification task, the winner of which [1] was an application of the multiclass boosting algorithm AdaBoost.MH [2]. It is believed that Linear Programming Boosting (LPBoost) is a more appropriate algorithm for this application due to the higher degree of sparsity in the solutions [3]. The present study aims to improve on the [1] result by using a similar feature set and the multiclass boosting algorithm LPBoost.MC. \\ References: [1] J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and K. Bal´azs. Aggregate features and ADABOOST for music classification. Machine Learning, 65 (2-3):473–484, 2006. [2] R.E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37:297–336, 1999. [3] Ayhan Demiriz, Kristin P. Bennett, and John Shawe-Taylor. Linear programming boosting via column generation. Machine Learning, 46(1–3):225–254, 2002. |
| 关 键 词: | 原始音频文件; 音乐信息检索评估交换; 线性规划增强 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-16：cjy |
| 阅读次数: | 65 |