Recently I have got the following nice song from my colleague. Somehow, he has resigned yesterday. It's quite coincidence with the song name. All the best to him. The lyric is also attached in this post. Enjoy~
Lyric
Thursday, December 10, 2009
Sunday, December 6, 2009
科系简介
是否在要申请大学时很彷徨，不知道要进入哪一个系呢？我是学电脑工程的，我一直对各个科系都很感兴趣。以下是我的一些关于各个科系内容的想法：
- 电脑工程(Computer Engineering):有效率地策划和执行重复的操作
- 材料工程(Material Engineering):测量和合成材料的性质
- 机械工程(Mechanical Engineering):设计和测量可动物件
- 土木工程(Civil Engineering):设计和测量巨型不动物件
- 电子工程(Electrical Engineering):设计电路
- 经济(Economic):发现并应用供需关系
- 生物(Biology):生物的演化与平衡
- 数学(Mathematic):发展量化的语言
- 物理(Physic):用量化语言表达和预测现象
Friday, December 4, 2009
Research Notes 7: Ensemble Learning for Data Stream Classification
Ensemble learning attempts to produce a strong learner by combining many weak learners. Ensemble learning can train stronger classification model on top of multiple simpler models such as decision trees, if there is significant diversity among the models.
In SEA (2001), it is suggested that even simple ensembles can produce results better that are comparable to individual classifiers. SEA is an ensemble learning scheme proposed for data stream classification. In SEA, a new classifier is trained whenever a new chunk of data arrives. The new classifiers are added to existing classifiers to form an ensemble until the memory is full. Whenever the memory is full, the new classifier is evaluated whether it should replaced one of the existing classifiers. The replacement can be considered as a way to handle concept drift. To decide which classifiers to be retained, SEA evaluate the classifiers with a novel heuristic that favors classifiers that can correctly classify instances on which the ensemble is nearly undecided. In the paper, SEA with C4.5 decision trees are tested.
In SEA (2001), it is suggested that even simple ensembles can produce results better that are comparable to individual classifiers. SEA is an ensemble learning scheme proposed for data stream classification. In SEA, a new classifier is trained whenever a new chunk of data arrives. The new classifiers are added to existing classifiers to form an ensemble until the memory is full. Whenever the memory is full, the new classifier is evaluated whether it should replaced one of the existing classifiers. The replacement can be considered as a way to handle concept drift. To decide which classifiers to be retained, SEA evaluate the classifiers with a novel heuristic that favors classifiers that can correctly classify instances on which the ensemble is nearly undecided. In the paper, SEA with C4.5 decision trees are tested.
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