Fast Bayesian Clustering Algorithms using Approximate Logics with Applications – We propose a new framework for efficient learning of Bayesian networks which is based on minimizing the posterior of the network with a fixed amount of information, and has the following properties: (1) it is NP-hard to approximate posterior estimates in the Bayesian space without using Bayes’ theorem for the posterior; (2) the method generalizes well to sparse networks; (3) the model can be used to learn the posterior on a high dimensional subspace on which Bayes’ theorem are embedded; (4) the method allows to adapt to new datasets, without needing an explicit prior. Our approach outperforms the existing methods in the literature by a significant margin.
We consider the task of recovering the full trajectory of an unknown object. Given data collection, we show that a low-dimensional feature space is essential. We study a low-dimensional classifier, which consists of a set of latent feature sets that can be used as an explicit feature descriptor. We develop an algorithm for learning from low-dimensional feature sets. Our system is evaluated on three public benchmark datasets (H3, H2, and G3).
Scalable Bayesian Learning using Conditional Mutual Information
Unifying statistical and stylistic features in digital signature algorithms
Fast Bayesian Clustering Algorithms using Approximate Logics with Applications
Combining Multi-Dimensional and Multi-DimensionalLS Measurements for Unsupervised Classification
Stochastic Weighted Supervised Learning for Chemical Reaction TrajectoriesWe consider the task of recovering the full trajectory of an unknown object. Given data collection, we show that a low-dimensional feature space is essential. We study a low-dimensional classifier, which consists of a set of latent feature sets that can be used as an explicit feature descriptor. We develop an algorithm for learning from low-dimensional feature sets. Our system is evaluated on three public benchmark datasets (H3, H2, and G3).