Fuzzy support vector machines for age and gender classification

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    10 Citations (Scopus)

    Abstract

    Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classi?cation problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the a Gender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.
    Original languageEnglish
    Title of host publicationINTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association
    EditorsTakao Kobayashi, Keikichi Hirose, Satoshi Nakamura
    Place of PublicationLisbon, Portugal
    PublisherInternational Speech Communication Association
    Pages2806-2809
    Number of pages4
    ISBN (Print)9781617821233
    Publication statusPublished - 2010
    EventINTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association - Makuhari, Makuhari, Japan
    Duration: 26 Sep 201030 Sep 2010

    Conference

    ConferenceINTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association
    CountryJapan
    CityMakuhari
    Period26/09/1030/09/10

    Fingerprint

    Support vector machines
    Positive ions
    Experiments

    Cite this

    Tran, D., & Sharma, D. (2010). Fuzzy support vector machines for age and gender classification. In T. Kobayashi, K. Hirose, & S. Nakamura (Eds.), INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association (pp. 2806-2809). Lisbon, Portugal: International Speech Communication Association.
    Tran, Dat ; Sharma, Dharmendra. / Fuzzy support vector machines for age and gender classification. INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association. editor / Takao Kobayashi ; Keikichi Hirose ; Satoshi Nakamura. Lisbon, Portugal : International Speech Communication Association, 2010. pp. 2806-2809
    @inproceedings{b956510a9a954c138738bea6a520fb75,
    title = "Fuzzy support vector machines for age and gender classification",
    abstract = "Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classi?cation problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the a Gender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.",
    author = "Dat Tran and Dharmendra Sharma",
    year = "2010",
    language = "English",
    isbn = "9781617821233",
    pages = "2806--2809",
    editor = "Takao Kobayashi and Keikichi Hirose and Satoshi Nakamura",
    booktitle = "INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association",
    publisher = "International Speech Communication Association",

    }

    Tran, D & Sharma, D 2010, Fuzzy support vector machines for age and gender classification. in T Kobayashi, K Hirose & S Nakamura (eds), INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association. International Speech Communication Association, Lisbon, Portugal, pp. 2806-2809, INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association, Makuhari, Japan, 26/09/10.

    Fuzzy support vector machines for age and gender classification. / Tran, Dat; Sharma, Dharmendra.

    INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association. ed. / Takao Kobayashi; Keikichi Hirose; Satoshi Nakamura. Lisbon, Portugal : International Speech Communication Association, 2010. p. 2806-2809.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    TY - GEN

    T1 - Fuzzy support vector machines for age and gender classification

    AU - Tran, Dat

    AU - Sharma, Dharmendra

    PY - 2010

    Y1 - 2010

    N2 - Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classi?cation problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the a Gender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.

    AB - Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classi?cation problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the a Gender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.

    M3 - Conference contribution

    SN - 9781617821233

    SP - 2806

    EP - 2809

    BT - INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association

    A2 - Kobayashi, Takao

    A2 - Hirose, Keikichi

    A2 - Nakamura, Satoshi

    PB - International Speech Communication Association

    CY - Lisbon, Portugal

    ER -

    Tran D, Sharma D. Fuzzy support vector machines for age and gender classification. In Kobayashi T, Hirose K, Nakamura S, editors, INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association. Lisbon, Portugal: International Speech Communication Association. 2010. p. 2806-2809