评论：提升算法：正则化，预测和模型拟合
Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
摘要：
作者正在为《统计科学》的读者提供一个真正的服务，它提供了一个写得很好的最新的关于提升的概述，起源于Freund和Schapire的开创性算法。同样，我们感谢高级软件，它将允许更大的读者试验或简单地应用提升启发的模型拟合。作者向我们展示了一个方法论的世界，说明了一个根本性的创新如何渗透到统计思维和实践的每一个角落。他们向读者介绍了一种特定的增强解释，然后展示了它的潜力，从分类（这一切都是从哪里开始的）扩展到最小二乘，指数家族模型，生存分析，到除树之外的基础学习者，如平滑样条，到自由度和正则化，以及模型选择的迷人近期工作。不了解的读者会发现，作者做了很好的工作，提出了一定的连贯和有用的解释提升。然而，另一位读者，谁已经看了一段时间的业务，可能会与作者的历史记录的细节，更重要的是，在他们对理论知识的现状的乐观。事实上，尽管“统计观点”已被证明是富有成效的，但它也导致了一些关于为什么提高工作效率的想法可能被误解，以及一些可能被误导的建议。[arXiv：0804.2752]
英文摘要：
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simply apply, boosting-inspired model fitting. The authors show us a world of methodology that illustrates how a fundamental innovation can penetrate every nook and cranny of statistical thinking and practice. They introduce the reader to one particular interpretation of boosting and then give a display of its potential with extensions from classification (where it all started) to least squares, exponential family models, survival analysis, to base-learners other than trees such as smoothing splines, to degrees of freedom and regularization, and to fascinating recent work in model selection. The uninitiated reader will find that the authors did a nice job of presenting a certain coherent and useful interpretation of boosting. The other reader, though, who has watched the business of boosting for a while, may have quibbles with the authors over details of the historic record and, more importantly, over their optimism about the current state of theoretical knowledge. In fact, as much as ``the statistical view'' has proven fruitful, it has also resulted in some ideas about why boosting works that may be misconceived, and in some recommendations that may be misguided. [arXiv:0804.2752]
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。
