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L. Mason, et al., “Functional Gradient Techniques for Combining Hypotheses,” In: A. J. Smola, et al., Eds., Advances in Large Margin Classifiers, MIT Press, Cambridge, 2000, pp. 221-246.

has been cited by the following article:

• TITLE: Splitting of Gaussian Models via Adapted BML Method Pertaining to Cry-Based Diagnostic System

  AUTHORS: Hesam Farsaie Alaie, Chakib Tadj

  KEYWORDS: Adapted Boosted Mixture Learning; Gaussian Mixture Model; Splitting of Gaussians; Expected-Maximization Algorithm; Cry Signals

  JOURNAL NAME: Engineering, Vol.5 No.10B, October 31, 2013

  ABSTRACT: In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.