کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
384956 660857 2012 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
On estimating simple probabilistic discriminative models with subclasses
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
On estimating simple probabilistic discriminative models with subclasses
چکیده انگلیسی

Discriminative subclass models can provide good estimates of complex ‘continuous to discrete’ conditional probabilities for hybrid Bayesian network models. However, the conventional approach of specifying deterministic ‘hard’ subclasses via unsupervised clustering can lead to inaccurate models. The multimodal softmax (MMS) model is presented as a new probabilistic discriminative subclass model that overcomes this unreliability. By invoking fully probabilistic latent ‘soft’ subclasses, MMS permits learning via standard statistical methods without requiring explicit clustering/relabeling of data. MMS is also shown to be closely related to the mixture of experts model and the generative Gaussian mixture classifier. Synthetic and benchmark classification results demonstrate the MMS model’s correctness and usefulness for hybrid probabilistic modeling.


► We propose multimodal softmax (MMS) models for hybrid Bayesian networks.
► Classifiers based on subclasses are probabilistically generalized by MMS.
► MMS more compact than related Gaussian mixture and mixture of expert models.
► MMS can be learned with statistical ML/MAP estimators and selection metrics.
► Accuracy and usefulness of MMS shown on difficult classification problems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 7, 1 June 2012, Pages 6659–6664
نویسندگان
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