Advances in Boosting（特邀演讲）
Advances in Boosting (Invited Talk)
摘要：
Boosting是一种生成许多简单分类规则并将它们组合成单个高精度规则的通用方法。在这次演讲中，我将回顾AdaBoost boosting算法及其一些基本理论，然后看看这个理论如何帮助我们面对在两个领域应用AdaBoost的一些挑战：在第一个领域，我们使用boosting来预测和建模复杂的交互拍卖中价格的不确定性。第二个应用程序是在电话口语对话系统中对呼叫者话语进行分类，在该系统中我们面临两个挑战：需要结合先验知识来补偿最初的数据不足;以及稍后需要过滤收集的大量未标记的示例流，以选择其标签可能是最有信息的示例。
英文摘要：
Boosting is a general method of generating many simple classification rules and combining them into a single, highly accurate rule. In this talk, I will review the AdaBoost boosting algorithm and some of its underlying theory, and then look at how this theory has helped us to face some of the challenges of applying AdaBoost in two domains: In the first of these, we used boosting for predicting and modeling the uncertainty of prices in complicated, interacting auctions. The second application was to the classification of caller utterances in a telephone spoken-dialogue system where we faced two challenges: the need to incorporate prior knowledge to compensate for initially insufficient data; and a later need to filter the large stream of unlabeled examples being collected to select the ones whose labels are likely to be most informative.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
