کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
386366 660883 2011 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
PBSVM: Partitioning and biased support vector machine for vocal fold pathology assessment using labeled and unlabeled data sets
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
PBSVM: Partitioning and biased support vector machine for vocal fold pathology assessment using labeled and unlabeled data sets
چکیده انگلیسی

Most of the existing classification methods, used for voice pathology assessment, are built based on labeled pathological and normal voice signals. This paper studies the problem of building a classifier using labeled and unlabeled data. We propose a novel learning technique, called Partitioning and Biased Support Vector Machine Classification (PBSVM), which tries to utilize all the available data in two steps: (1) a new heuristically partition-based algorithm, which extracts high quality pathological and normal samples from an unlabeled set, and (2) a more principle approach based on biased formulation of support vector machine, which is fairly robust to mislabeling and unbalance data problem. Experiments with wavelet-based energy features extracted from sustained vowels show that the new recognition scheme is highly feasible and significantly outperform the baseline classical SVM classifier, especially in the situation where the labeled training data is small.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 38, Issue 1, January 2011, Pages 610–619
نویسندگان
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