Apr 13, 2020 · Our naive learning algorithm not only has higher accuracies, lower learning, and classification times but also has simple and intuitive representation ability.
Briefly, we learn the CB‐decomposable MBC model by dividing it into three components: class subgraph, bridge subgraph, and feature subgraph. First, we analyze ...
A naive learning algorithm for class-bridge-decomposable multidimensional Bayesian network classifiers. Y. Lv, W. Hu, J. Liang, Y. Qian, and J. Miao.
A naive learning algorithm for class‐bridge‐decomposable multidimensional Bayesian network classifiers ... Cui Z., Zhang J., Wu D., Cai X., Wang H., Zhang W., ...
In this paper, we propose a novel learning algorithm for class-bridge (CB) decomposable MBCs into maximal connected components. Basically, based on a wrapper ...
Section 3 presents a structural learning algorithm that uses the class-brigde decomposability to incrementally build a complex network structure while saving.
In this paper, we prove that class-bridge decomposability can also be used to guarantee the tractability of the models. We also propose a strategy for ...
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A class-bridge decomposable multidimensional Gaussian net- work is presented as an interpretable and powerful model, to account for the morphological di ...
Jun 25, 2024 · Multi-dimensional classification (MDC) aims at learning from objects where each of them is represented by a single instance while associated with multiple ...
Multi-dimensional classification aims at finding a function that assigns a vector of class values to a given vector of features.