26 citations · 45 across the 3 of their papers we have counts for
6 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…
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…