In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression for graphs, which is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics
This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regressi...
In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems...
Motivation: Gene selection algorithms for cancer classification, based on the expression of a small ...
In this paper we deal with graph classification. We propose a new algorithm for performing sparse lo...
In this thesis, sparse logistic regression models are applied in a set of real world machine learnin...
In this paper, we propose a novel method for sparse logistic regression with non-convex regularizati...
Multinomial logistic regression provides the standard penalised maximumlikelihood solution to multi-...
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they u...
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they u...
Although popular and extremely well established in mainstream statistical data analysis, logistic re...
In this paper, we propose a novel method for sparse logistic regression with non-convex reg-ularizat...
Motivation: This paper gives a new and efficient algorithm for the sparse logistic regression probl...
The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining ...
MI: Global COE Program Education-and-Research Hub for Mathematics-for-IndustryグローバルCOEプログラム「マス･フォア･イ...
Over the past few decades, a large number of algorithms have been developed for dimensionality reduc...
This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regressi...
In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems...
Motivation: Gene selection algorithms for cancer classification, based on the expression of a small ...
In this paper we deal with graph classification. We propose a new algorithm for performing sparse lo...
In this thesis, sparse logistic regression models are applied in a set of real world machine learnin...
In this paper, we propose a novel method for sparse logistic regression with non-convex regularizati...
Multinomial logistic regression provides the standard penalised maximumlikelihood solution to multi-...
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they u...
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they u...
Although popular and extremely well established in mainstream statistical data analysis, logistic re...
In this paper, we propose a novel method for sparse logistic regression with non-convex reg-ularizat...
Motivation: This paper gives a new and efficient algorithm for the sparse logistic regression probl...
The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining ...
MI: Global COE Program Education-and-Research Hub for Mathematics-for-IndustryグローバルCOEプログラム「マス･フォア･イ...
Over the past few decades, a large number of algorithms have been developed for dimensionality reduc...
This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regressi...
In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems...
Motivation: Gene selection algorithms for cancer classification, based on the expression of a small ...
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