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Found 8 papers, 0 papers with code
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process
Bagging independently constructs decision trees without evaluating their combination performance and averages them afterward.
Boosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees
Thus, it is expected that ensembles of meta-trees are more effective in improving predictive performance than a single meta-tree, and there are no previous studies that construct multiple meta-trees in boosting.
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are usually used only to represent prediction functions to be constructed from given data.
Batch Updating of a Posterior Tree Distribution over a Meta-Tree
Previously, we proposed a probabilistic data generation model represented by an unobservable tree and a sequential updating method to calculate a posterior distribution over a set of trees.
Stochastic 2D Signal Generative Model with Wavelet Packets Basis Regarded as a Random Variable and Bayes Optimal Processing
When the basis is unknown the candidate of basis increases in exponential order with respect to the signal size.
Probability Distribution on Rooted Trees
The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning.
Probability Distribution on Full Rooted Trees
Its parametric representation is suitable for calculating the properties of our distribution using recursive functions, such as the mode, expectation, and posterior distribution.
Theoretical Analysis of the Advantage of Deepening Neural Networks
We propose two new criteria to understand the advantage of deepening neural networks.
