图 书 馆
准增量贝叶斯分类器Quasi-Incremental Bayesian Classifier |
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| 课程网址: | http://videolectures.net/ecml07_hruschka_qibc/ |
| 主讲教师: | Estevam R. Hruschka |
| 开课单位: | 圣卡洛斯联邦大学 |
| 开课时间: | 2008-01-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This talk describes and empirically evaluates a Quasi-Incremental Bayesian Classifier (QBC) designed to be used when a classification task must be performed in dynamic systems such as sensor networks, which are continuously receiving new piece of information to be stored in huge databases. Therefore, the knowledge that needs to be extracted from these databases is continuously evolving and the learning process may need to go on almost indefinitely. The induction proposed by QBC is performed in two steps; in the first one a traditional Bayesian Network (BN) induction algorithm is performed using an initial amount of data. As far as new data is available, only the numerical parameters of the classifier are updated. The conducted experiments showed that QBC tends to maintain the average correct classification rates obtained with non-incremental classifiers while decreasing the time needed to induce the classifier. |
| 关 键 词: | 贝叶斯分类器; 机器学习; 传感器网络 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：heyf |
| 阅读次数: | 40 |