scholarly journals Semi-supervised learning via penalized mixture model with application to microarray sample classification

2006 ◽  
Vol 22 (19) ◽  
pp. 2388-2395 ◽  
Author(s):  
W. Pan ◽  
X. Shen ◽  
A. Jiang ◽  
R. P. Hebbel
Keyword(s):  
Supervised Learning ◽  
Mixture Model ◽  
Sample Classification ◽  
Microarray Sample

The supervised learning Gaussian mixture model

1998 ◽  
Vol 13 (5) ◽  
pp. 471-474 ◽  
Author(s):  
Jiyong Ma ◽  
Wen Gao
Keyword(s):  
Supervised Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

Adaptive Mixtures of Probabilistic Transducers

1997 ◽  
Vol 9 (8) ◽  
pp. 1711-1733 ◽  
Author(s):  
Yoram Singer
Keyword(s):  
Online Learning ◽  
Theoretical Analysis ◽  
Supervised Learning ◽  
Mixture Model ◽  
Noun Phrase ◽  
Learning Algorithm ◽  
Online Learning Algorithm

We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an online learning algorithm that efficiently infers the structure and estimates the parameters of each probabilistic transducer in the mixture. Theoretical analysis and comparative simulations indicate that the learning algorithm tracks the best transducer from an arbitrarily large (possibly infinite) pool of models. We also present an application of the model for inducing a noun phrase recognizer.

scholarly journals A Beta-mixture model for dimensionality reduction, sample classification and analysis

2011 ◽  
Vol 12 (1) ◽  
pp. 215 ◽  
Author(s):  
Kirsti Laurila ◽  
Bodil Oster ◽  
Claus L Andersen ◽  
Philippe Lamy ◽  
Torben Orntoft ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Mixture Model ◽  
Sample Classification ◽  
Beta Mixture Model

scholarly journals Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Adama Nouboukpo ◽  
Mohand Saïd Allili
Keyword(s):  
Supervised Learning ◽  
Mixture Models ◽  
Mixture Model ◽  
Real World ◽  
Synthetic Data ◽  
Unlabelled Data ◽  
Multi Level ◽  
Weakly Supervised ◽  
And Performance ◽  
Class Labels

We propose a new weakly supervised approach for classification and clustering based on mixture models. Ourapproach integrates multi-level pairwise group and classconstraints between samples to learn the underlyinggroup structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes thenumber of classes is known but does not assume anyprior knowledge about the number of mixture components in each class. Therefore, our model : (1) allocatesmultiple mixture components to individual classes, (2)estimates automatically the number of components ofeach class, 3) propagates class labels to unlabelled datain a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasetsshow the robustness and performance of our model overstate-of-the-art methods.

scholarly journals Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 789-801
Author(s):  
Zhi Li ◽  
Liqun Yang ◽  
Zhoujun Li
Keyword(s):  
Supervised Learning ◽  
Mixture Model ◽  
Privacy Preserving ◽  
Model Based

scholarly journals Semi-Supervised Learning Using Hierarchical Mixture Models: Gene Essentiality Case Study

2021 ◽  
Vol 26 (2) ◽  
pp. 40
Author(s):  
Michael W. Daniels ◽  
Daniel Dvorkin ◽  
Rani K. Powers ◽  
Katerina Kechris
Keyword(s):  
Supervised Learning ◽  
Mixture Model ◽  
Essential Genes ◽  
Training Data ◽  
Data Sets ◽  
Data Types ◽  
Gene Essentiality ◽  
Multiple Data ◽  
Level Data ◽  
Accuracy Of Prediction

Integrating gene-level data is useful for predicting the role of genes in biological processes. This problem has typically focused on supervised classification, which requires large training sets of positive and negative examples. However, training data sets that are too small for supervised approaches can still provide valuable information. We describe a hierarchical mixture model that uses limited positively labeled gene training data for semi-supervised learning. We focus on the problem of predicting essential genes, where a gene is required for the survival of an organism under particular conditions. We applied cross-validation and found that the inclusion of positively labeled samples in a semi-supervised learning framework with the hierarchical mixture model improves the detection of essential genes compared to unsupervised, supervised, and other semi-supervised approaches. There was also improved prediction performance when genes are incorrectly assumed to be non-essential. Our comparisons indicate that the incorporation of even small amounts of existing knowledge improves the accuracy of prediction and decreases variability in predictions. Although we focused on gene essentiality, the hierarchical mixture model and semi-supervised framework is standard for problems focused on prediction of genes or other features, with multiple data types characterizing the feature, and a small set of positive labels.

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