图 书 馆
点对点网络中的分布式分类研究Distributed Classification in Peer-to-Peer Networks |
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| 课程网址: | http://videolectures.net/kdd07_luo_dcip/ |
| 主讲教师: | Ping Luo |
| 开课单位: | 中国科学院 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This work studies the problem of distributed classification in peer-to-peer (P2P) networks. While there has been a significant amount of work in distributed classification, most of existing algorithms are not designed for P2P networks. Indeed, as server-less and router-less systems, P2P networks impose several challenges for distributed classification: (1) it is not practical to have global synchronization in large- scale P2P networks; (2) there are frequent topology changes caused by frequent failure and recovery of peers; and (3) there are frequent on-the-fly data updates on each peer. In this paper, we propose an ensemble paradigm for distributed classification in P2P networks. Under this paradigm, each peer builds its local classifiers on the local data and the results from all local classifiers are then combined by plurality voting. To build local classifiers, we adopt the learning algorithm of pasting bites to generate multiple local classifiers on each peer based on the local data. To combine local results, we propose a general form of Distributed Plurality Voting (DPV ) protocol in dynamic P2P networks. This protocol keeps the single-site validity for dynamic networks, and supports the computing modes of both one-shot query and continuous monitoring. We theoretically prove that the condition C0 for sending messages used in DPV0 is locally communication-optimal to achieve the above properties. Finally, experimental results on real-world P2P networks show that: (1) the proposed ensemble paradigm is effective even if there are thousands of local classifiers; (2) in most cases, the DPV0 algorithm is local in the sense that voting is processed using information gathered from a very small vicinity, whose size is independent of the network size; (3) DPV0 is significantly more communication-efficient than existing algorithms for distributed plurality voting. |
| 关 键 词: | 分布式分类; 拓扑结构; 集成模式 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-18：cwx |
| 阅读次数: | 39 |