36 citations · 128 across the 18 of their papers we have counts for
18 papers
Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning
Kai Ming Ting, Jonathan R. Wells, Takashi Washio
Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. A curren…
A new simple and effective measure for bag-of-word inter-document similarity measurement
Sunil Aryal, Kai Ming Ting, Takashi Washio +1
To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarit…
Free-hand gas identification based on transfer function ratios without gas flow control
Gaku Imamura, Kota Shiba, Genki Yoshikawa +1
Gas identification is one of the most important functions of gas sensor systems. To identify gas species from sensing signals, however, gas input patterns (e.g. the gas flow sequen…
Learning Graph Representation via Formal Concept Analysis
Yuka Yoneda, Mahito Sugiyama, Takashi Washio
We present a novel method that can learn a graph representation from multivariate data. In our representation, each node represents a cluster of data points and each edge represent…
Analysis of cause-effect inference by comparing regression errors
Patrick Blöbaum, Dominik Janzing, Takashi Washio +2
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assum…
Error Asymmetry in Causal and Anticausal Regression
Patrick Blöbaum, Takashi Washio, Shohei Shimizu
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated. Recent wo…
A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
Naoki Tanaka, Shohei Shimizu, Takashi Washio
A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data. A linea…
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Takanori Inazumi, Takashi Washio, Shohei Shimizu +3
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to di…
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
Joe Suzuki, Takanori Inazumi, Takashi Washio +1
The notion of causality is used in many situations dealing with uncertainty. We consider the problem whether causality can be identified given data set generated by discrete random…
Anomaly detection in reconstructed quantum states using a machine-learning technique
Satoshi Hara, Takafumi Ono, Ryo Okamoto +2
The accurate detection of small deviations in given density matrices is important for quantum information processing. Here we propose a new method based on the concept of data mini…
ParceLiNGAM: A causal ordering method robust against latent confounders
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen +1
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal…
Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen +1
We consider to learn a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal…
Learning a Common Substructure of Multiple Graphical Gaussian Models
Satoshi Hara, Takashi Washio
Properties of data are frequently seen to vary depending on the sampled situations, which usually changes along a time evolution or owing to environmental effects. One way to analy…
Discovering causal structures in binary exclusive-or skew acyclic models
Takanori Inazumi, Takashi Washio, Shohei Shimizu +3
Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discove…
GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search
Akihiro Inokuchi, Hiroaki Ikuta, Takashi Washio
The mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequ…
Prismatic Algorithm for Discrete D.C. Programming Problems
Yoshinobu Kawahara, Takashi Washio
In this paper, we propose the first exact algorithm for minimizing the difference of two submodular functions (D.S.), i.e., the discrete version of the D.C. programming problem. Th…
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa +5
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typic…
GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu +1
Finding the structure of a graphical model has been received much attention in many fields. Recently, it is reported that the non-Gaussianity of data enables us to identify the str…