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Active Learning Approach

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Xi-zhao Wang – One of the best experts on this subject based on the ideXlab platform.

  • A new and informative Active Learning Approach for support vector machine
    Information Sciences, 2013
    Co-Authors: Xi-zhao Wang
    Abstract:

    Abstract Active Learning Approach has been integrated with support vector machine or other machine-Learning techniques in many areas. However, the challenge is: Unlabeled instances are often abundant or easy to obtain, but their labels are expensive and time-consuming to get in general. In spite of this, most existing methods cannot guarantee the usefulness of each query in Learning a new classifier. In this paper, we propose a new Active Learning Approach of selecting the most informative query for annotation. Unlabeled instance, which is nearest to the support vector machine’s hyperplane learnt from both the unlabeled instance itself and all labeled instances, is selected as the query for annotation. Merits of these queries in Learning a new optimal hyperplane have been assured before they are annotated and put into the training set. Experimental results on several UCI datasets have shown the efficiency of our Approach.

Tu Zhi – One of the best experts on this subject based on the ideXlab platform.

  • A New Support Vector Machines Active Learning Approach and its Application in Text Classification
    Computer Science, 2003
    Co-Authors: Tu Zhi
    Abstract:

    There are two well-known characteristics about text classification. One is that the dimension of the sample space is very high, while the number of examples available usually is very small. The other is that the example vectors are sparse. Meanwhile, we find existing support vector machines Active Learning Approaches are subject to the influence of outliers. Based on these observations, this paper presents a new hybrid Active Learning Approach. In this Approach, to select the unlabelled example(s) to query, the learner takes into account both sparseness and high-dimension characteristics of examples as well as its uncertainty about the examples’ categorization. This way, the Active learner needs less labeled examples, but still can get a good generalization performance more quickly than competing methods. Our empirical results indicate that this new Approach is effective.

Sven Seuken – One of the best experts on this subject based on the ideXlab platform.

  • an Active Learning Approach to home heating in the smart grid
    International Joint Conference on Artificial Intelligence, 2013
    Co-Authors: Mike Shann, Sven Seuken
    Abstract:

    A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible Approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an Active Learning Approach to adjust the home temperature in a semi-automatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard Approaches from the Active Learning literature.

  • IJCAI – An Active Learning Approach to home heating in the smart grid
    , 2013
    Co-Authors: Mike Shann, Sven Seuken
    Abstract:

    A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible Approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an Active Learning Approach to adjust the home temperature in a semi-automatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard Approaches from the Active Learning literature.

Mike Shann – One of the best experts on this subject based on the ideXlab platform.

  • an Active Learning Approach to home heating in the smart grid
    International Joint Conference on Artificial Intelligence, 2013
    Co-Authors: Mike Shann, Sven Seuken
    Abstract:

    A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible Approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an Active Learning Approach to adjust the home temperature in a semi-automatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard Approaches from the Active Learning literature.

  • IJCAI – An Active Learning Approach to home heating in the smart grid
    , 2013
    Co-Authors: Mike Shann, Sven Seuken
    Abstract:

    A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible Approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an Active Learning Approach to adjust the home temperature in a semi-automatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard Approaches from the Active Learning literature.

Laurie O. Campbell – One of the best experts on this subject based on the ideXlab platform.

  • Student-created video: an Active Learning Approach in online environments
    Interactive Learning Environments, 2020
    Co-Authors: Laurie O. Campbell, Samantha Heller, Lindsay Pulse
    Abstract:

    The purpose of this study was to investigate student-created video as an Active Learning Approach in an online environment to inform instructional practices of student-created video in STEM. Data a…

  • Implementing Student-Created Video in Engineering: An Active Learning Approach for Exam Preparedness
    International Journal of Engineering Pedagogy (iJEP), 2019
    Co-Authors: Laurie O. Campbell, Samantha Heller, Ronald F. Demara
    Abstract:

    In this case study, an Active Learning Approach to exam preparation in engineering was investigated. The Learner Video Thumbnailing (LVT) strategy incorporated video blogs (vlogs) to reinforce course content. In this innovative method, students voluntarily choose one of two roles as either the role of a spectator (watching the vlogs) n=69 or the role of a vlogger (creating the vlogs) n=8 to earn extra credit on a formative exam. Data collected in this study included the vlogs, scores on the achievement questions, and a post-interview of the vloggers. Differences in video development by gender were identified. The use of the LVT Approach promoted improved achievement and student engagement.