Iku Ohama

Discovering Co-Cluster Structure from Relationships between Biased Objects

Hiroki Arimura, Takuya Kida, Iku Ohama, 2018, IEICE Trans. Inf. Syst..

Discovering Relevance-Dependent Bicluster Structure from Relational Data: A Model and Algorithm

Hiroki Arimura, Takuya Kida, Iku Ohama, 2018, Transactions of the Japanese Society for Artificial Intelligence.

Multi-viewpoint Silhouette Extraction with 3D Context-aware Error Detection, Correction, and Shadow Suppression

Takashi Matsuyama, Yoshiyuki Tsuda, Shohei Nobuhara, 2009, IPSJ Trans. Comput. Vis. Appl..

An Extension of the Infinite Relational Model Incorporating Interaction between Objects

Hiroki Arimura, Takuya Kida, Iku Ohama, 2013, PAKDD.

Discovering Relevance-Dependent Bicluster Structure from Relational Data

Hiroki Arimura, Takuya Kida, Iku Ohama, 2017, IJCAI.

A machine learning approach for lighting perception analysis via crowdsourcing

Iku Ohama, Yuki Minoda, Eiichi Muramoto, 2015, UbiComp/ISWC Adjunct.

Multi-Layered Framework for Modeling Relationships between Biased Objects

Hiroki Arimura, Takuya Kida, Iku Ohama, 2015, SDM.

An Extension of Gradient Boosted Decision Tree Incorporating Statistical Tests

Tadahiro Taniguchi, Iku Ohama, Ryuji Sakata, 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

On the Model Shrinkage Effect of Gamma Process Edge Partition Models

Hiroki Arimura, Takuya Kida, Issei Sato, 2017, NIPS.

The Relevance Dependent Infinite Relational Model for Discovering Co-Cluster Structure from Relationships with Structured Noise

Hiroki Arimura, Takuya Kida, Iku Ohama, 2016, IEICE Trans. Inf. Syst..