图 书 馆
利用Choquet积分学习单调非线性模型Learning Monotone Nonlinear Models using the Choquet Integral |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_cheng_learning/ |
| 主讲教师: | Weiwei Cheng |
| 开课单位: | 菲利普森马尔堡大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The learning of predictive models that guarantee monotonicity in the input variables has received increasing attention in machine learning in recent years. While the incorporation of monotonicity constraints is rather simple for certain types of models, it may become a more intricate problem for others. By trend, the difficulty of ensuring monotonicity increases with the flexibility or, say, nonlinearity of a model. In this paper, we advocate the so-called Choquet integral as a tool for learning monotone nonlinear models. While being widely used as a flexible aggregation operator in different fields, such as multiple criteria decision making, the Choquet integral is much less known in machine learning so far. Apart from combining monotonicity and flexibility in a mathematically sound and elegant manner, the Choquet integral has additional features making it attractive from a machine learning point of view. Notably, it offers measures for quantifying the importance of individual predictor variables and the interaction between groups of variables. As a concrete application of the Choquet integral, we propose a generalization of logistic regression. The basic idea of our approach, referred to as choquistic regression, is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive class, by the Choquet integral. |
| 关 键 词: | 预测模型; 灵活聚集算子; 趋势 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-02：cwx |
| 阅读次数: | 32 |