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Operations for Learning with Graphical Models
The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.
Learning classification trees
This paper introduces Bayesian techniques for splitting, smoothing, and tree averaging, which are similar to Quinlan's information gain, while smoothing and averaging replace pruning.
Improving LDA topic models for microblogs via tweet pooling and automatic labeling
This paper empirically establishes that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a range of pooling schemes.
Variational Extensions to EM and Multinomial PCA
This paper begins with a review of the basic theory of the variational extension to the expectation-maximization algorithm, and then presents discrete component finding algorithms in that light.
A Guide to the Literature on Learning Probabilistic Networks from Data
The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic
A theory of learning classification rules
A Bayesian theory of learning classi cation rules, the comparison and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learningclass probability trees and bounding error when learning logical rules are reported.
Bayesian Back-Propagation
Connectionist feed-forward networks, t rained with backpropagat ion, can be used both for nonlinear regression and for (discrete one-of-C ) classification. This paper presents approximate Bayesian
Unsupervised Object Discovery: A Comparison
The goal of this paper is to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns, and a rigorous framework for evaluating unsupervised object discovery methods is proposed.