9 Result(s)

within Hsuan-Tien Lin

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  1. No Access

    Article

    Progressive random k-labelsets for cost-sensitive multi-label classification

    In multi-label classification, an instance is associated with multiple relevant labels, and the goal is to predict these labels simultaneously. Many real-world applications of multi-label classification come w...

    Yu-Ping Wu, Hsuan-Tien Lin in Machine Learning (2017)

  2. No Access

    Chapter and Conference Paper

    Linear Upper Confidence Bound Algorithm for Contextual Bandit Problem with Piled Rewards

    We study the contextual bandit problem with linear payoff function. In the traditional contextual bandit problem, the algorithm iteratively chooses an action based on the observed context, and immediately rece...

    Kuan-Hao Huang, Hsuan-Tien Lin in Advances in Knowledge Discovery and Data Mining (2016)

  3. No Access

    Chapter and Conference Paper

    A Simple Unlearning Framework for Online Learning Under Concept Drifts

    Real-world online learning applications often face data coming from changing target functions or distributions. Such changes, called the concept drift, degrade the performance of traditional online learning al...

    Sheng-Chi You, Hsuan-Tien Lin in Advances in Knowledge Discovery and Data Mining (2016)

  4. No Access

    Article

    Improving ranking performance with cost-sensitive ordinal classification via regression

    This paper proposes a novel ranking approach, cost-sensitive ordinal classification via regression (COCR), which respects the discrete nature of ordinal ranks in real-world data sets. In particular, COCR appli...

    Yu-Xun Ruan, Hsuan-Tien Lin, Ming-Feng Tsai in Information Retrieval (2014)

  5. No Access

    Chapter and Conference Paper

    Machine Learning Approaches for Interactive Verification

    Interactive verification is a new problem, which is closely related to active learning, but aims to query as many positive instances as possible within some limited query budget. We point out the similarity be...

    Yu-Cheng Chou, Hsuan-Tien Lin in Advances in Knowledge Discovery and Data Mining (2014)

  6. Article

    A note on Platt’s probabilistic outputs for support vector machines

    Platt’s probabilistic outputs for Support Vector Machines (Platt, J. in Smola, A., et al. (eds.) Advances in large margin classifiers. Cambridge, 2000) has been popular for applications that require posterior cl...

    Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng in Machine Learning (2007)

  7. No Access

    Chapter and Conference Paper

    Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice

    We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thresholds. We derive novel large-margin boun...

    Hsuan-Tien Lin, Ling Li in Algorithmic Learning Theory (2006)

  8. No Access

    Chapter and Conference Paper

    Improving Generalization by Data Categorization

    In most of the learning algorithms, examples in the training set are treated equally. Some examples, however, carry more reliable or critical information about the target than the others, and some may carry wr...

    Ling Li, Amrit Pratap, Hsuan-Tien Lin in Knowledge Discovery in Databases: PKDD 2005 (2005)

  9. No Access

    Chapter and Conference Paper

    Infinite Ensemble Learning with Support Vector Machines

    Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of base hypotheses. However, existing algorithms are limited to combining only a finite number of ...

    Hsuan-Tien Lin, Ling Li in Machine Learning: ECML 2005 (2005)