Covering algorithms for learning rule sets tend toward learning concise rule sets based on the training data. This bias may not be appropriate in the domain of text classification due to the large number of informative features these domains typically contain. We present a basic covering algorithm, DAIRY, that learns unordered rule sets, and present two extensions that encourage the rule learner to milk the training data to varying degrees, by recycling covered training data, and by searching for completely redundant but highly accurate rules. We evaluate these modifications on web page and newsgroup recommendation problems and show recycling can improve classification accuracy by over 10%. Redundant rule learning provides smaller increases in most datasets, but may decrease accuracy in some.
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机译:用于折叠标签的不干胶纸-是透明材料,具有两个彼此相邻的文本面板,并且在相对的侧面覆盖面板AB DE4211373C纸(1)具有两个部分(2a,2b)连接在一起,每个部分都被横向折叠分开将第(3)行插入到text(4a,4b)和cover(5a,5b)面板中。它是透明材料,各部分相互错开。文本面板彼此并排,而一个覆盖面板在其文本面板上方,另一覆盖面板在其下方。文本面板之间可以有一个固定凸耳,彼此对齐的部分从固定凸耳从相对的侧面伸出。每个面板的边缘附近可以有一个穿孔,在折叠位置上每个穿孔都在另一个穿孔之上。优点-文字可以抵抗液体,例如用于清洁。 AN 93328649 TI用于摄像机的视频图像闪烁减少系统-使用检测到的相移幅度来控制视频输出信号的可变放大器