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Track 1 of KDDCup 2011 aims at predicting the rating behavior of users in the Yahoo! Music system. At National Taiwan University, we organize a course that teams up students to work on both tracks of KDDCup 2011. For track 1, we first tackle the problem by building variants of existing individual models We then blend the individual models along with some(More)
The track 2 problem in KDD Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history but also the taxonomic information of track, artist, album, and genre. This paper describes(More)
We formulate a framework for applying error-correcting codes (ECCs) on multilabel classification problems. The framework treats some base learners as noisy channels and uses ECC to correct the prediction errors made by the learners. The framework immediately leads to a novel ECC-based explanation of the popular random k-label sets (RAKEL) algorithm using a(More)
Efficient image search clustering is prominent for image search engines for exponentially growing photo collections. In this work, we propose an image search clustering approach which selects multiple canonical images from image search results and constructs image clusters in real time on an image sub-graph for the search results. The efficiency is achieved(More)
This paper describes our ensemble of three classifiers for the KDD Cup 2009 challenge. First, we transform the three binary classification tasks into a joint multi-class classification problem, and solve an l1-regularized maximum entropy model under the LIBLINEAR framework. Second, we propose a heterogeneous base learner, which is capable of handling(More)
The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently(More)
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