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of 41
pro vyhledávání: '"Nakahara, Yuta"'
Autor:
Nakahara, Yuta
The tree-structured stick-breaking process (TS-SBP) mixture model is a non-parametric Bayesian model that can represent tree-like hierarchical structures among the mixture components. For TS-SBP mixture models, only a Markov chain Monte Carlo (MCMC)
Externí odkaz:
http://arxiv.org/abs/2405.00385
Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are bagging and boosting. Bagging independently constructs decision trees w
Externí odkaz:
http://arxiv.org/abs/2402.06452
A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-tree is recently proposed. It solves the problem of overfitting caused
Externí odkaz:
http://arxiv.org/abs/2402.06386
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
Externí odkaz:
http://arxiv.org/abs/2306.07060
Autor:
Nakahara, Yuta, Matsushima, Toshiyasu
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. The set is called a meta-tree. In this paper, we propose a m
Externí odkaz:
http://arxiv.org/abs/2303.09705
This study deals with two-dimensional (2D) signal processing using the wavelet packet transform. When the basis is unknown the candidate of basis increases in exponential order with respect to the signal size. Previous studies do not consider the bas
Externí odkaz:
http://arxiv.org/abs/2202.00568
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. On the other hand, such hierarchical expressive cap
Externí odkaz:
http://arxiv.org/abs/2201.09460
Publikováno v:
Entropy 2022, 24(3), 328
The recursive and hierarchical structure of full rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. In most of these cases, the full rooted tree is not a rand
Externí odkaz:
http://arxiv.org/abs/2109.12825
Autor:
Nakahara, Yuta, Matsushima, Toshiyasu
Publikováno v:
Entropy 2021, 23, 991
In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, the researchers have mainly focused on the coding procedure that o
Externí odkaz:
http://arxiv.org/abs/2106.03349
Publikováno v:
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 479-484
We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in order to understand the advantage of deepening neural networks. Unless d
Externí odkaz:
http://arxiv.org/abs/2009.11479