Ichigaku Takigawa

发表

Algorithms for Finding a Minimum Repetition Representation of a String

Atsuyoshi Nakamura, Mineichi Kudo, Tomoya Saito, 2010, SPIRE.

Performance analysis of minimum /spl lscr//sub 1/-norm solutions for underdetermined source separation

Mineichi Kudo, Jun Toyama, Ichigaku Takigawa, 2004, IEEE Transactions on Signal Processing.

Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set

Ichigaku Takigawa, Hiroshi Mamitsuka, Hiroshi Mamitsuka, 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Graph mining: procedure, application to drug discovery and recent advances.

Ichigaku Takigawa, Hiroshi Mamitsuka, Hiroshi Mamitsuka, 2013, Drug discovery today.

Efficiently mining δ-tolerance closed frequent subgraphs

Ichigaku Takigawa, Hiroshi Mamitsuka, Hiroshi Mamitsuka, 2011, Machine Learning.

Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data

Ichigaku Takigawa, Hiroshi Mamitsuka, Raymond Wan, 2006, VDMB.

A modified LEGION using a spectrogram for speech segregation

Jun Toyama, Ichigaku Takigawa, M. Shimbo, 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String

Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka, 2020, AAAI.

Hao Ding, Ichigaku Takigawa, Hiroshi Mamitsuka, 2014, Briefings Bioinform..

A spectral clustering approach to optimally combining numericalvectors with a modular network

Ichigaku Takigawa, Hiroshi Mamitsuka, Motoki Shiga, 2007, KDD '07.

Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces

Akira Tanaka, Hideyuki Imai, Mineichi Kudo, 2004, KES.

Mining approximate patterns with frequent locally optimal occurrences

Atsuyoshi Nakamura, Mineichi Kudo, Ichigaku Takigawa, 2016, Discret. Appl. Math..

An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting

Masanori Sugimoto, Hideyuki Imai, Mineichi Kudo, 2017, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Theoretical Analyses on Ensemble and Multiple Kernel Regressors

Akira Tanaka, Hideyuki Imai, Mineichi Kudo, 2014, ACML.

Dense core model for cohesive subgraph discovery

Hideyuki Imai, Mineichi Kudo, Ichigaku Takigawa, 2016, Soc. Networks.

Convex sets as prototypes for classifying patterns

Atsuyoshi Nakamura, Mineichi Kudo, Ichigaku Takigawa, 2009, Eng. Appl. Artif. Intell..

Mining significant tree patterns in carbohydrate sugar chains

Minoru Kanehisa, Ichigaku Takigawa, Hiroshi Mamitsuka, 2008, ECCB.

Dual Convolutional Neural Network for Graph of Graphs Link Prediction pdf

Yoshihiro Yamanishi, Hisashi Kashima, Yukino Baba, 2018, ArXiv.

Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102,400 nodes pdf

Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Teramoto, 2020, Soft Computing.

A spectral approach to clustering numerical vectors as nodes in a network

Ichigaku Takigawa, Hiroshi Mamitsuka, Motoki Shiga, 2011, Pattern Recognit..

Field independent probabilistic model for clustering multi-field documents

Jia Zeng, Ichigaku Takigawa, Hiroshi Mamitsuka, 2009, Inf. Process. Manag..

Ensemble and Multiple Kernel Regressors: Which Is Better?

Akira Tanaka, Hideyuki Imai, Mineichi Kudo, 2015, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Community Change Detection in Dynamic Networks in Noisy Environment

Hideyuki Imai, Mineichi Kudo, Ichigaku Takigawa, 2015, WWW.

Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval

Ichigaku Takigawa, Hiroshi Mamitsuka, Raymond Wan, 2006, TREC.

Dual graph convolutional neural network for predicting chemical networks

Yoshihiro Yamanishi, Hisashi Kashima, Shonosuke Harada, 2020, BMC Bioinformatics.

Reducing Redundancy with Unit Merging for Self-constructive Normalized Gaussian Networks

Masanori Sugimoto, Hideyuki Imai, Mineichi Kudo, 2016, ICANN.

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Takashi Kamachi, Ichigaku Takigawa, Takashi Toyao, 2020 .

Machine learning reveals orbital interaction in materials

Kiyoyuki Terakura, Koji Tsuda, Ichigaku Takigawa, 2017, Science and technology of advanced materials.

Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data

Koji Tsuda, Ichigaku Takigawa, Hiroshi Mamitsuka, 2009, Bioinform..

Machine Learning and Optimal Design of Experiments for Heterogeneous Catalysis

Ichigaku Takigawa, 2019 .

Graph Minors from Simulated Annealing for Annealing Machines with Sparse Connectivity

Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Teramoto, 2018, TPNC.

Analyses on Generalization Error of Ensemble Kernel Regressors

Akira Tanaka, Hideyuki Imai, Mineichi Kudo, 2014, S+SSPR.

Mining metabolic pathways through gene expression

Ichigaku Takigawa, Hiroshi Mamitsuka, Timothy Hancock, 2010, Bioinform..

Identifying pathways of coordinated gene expression.

Ichigaku Takigawa, Timothy Hancock, Hiroshi Mamitsuka, 2013, Methods in molecular biology.

Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates

Masanori Sugimoto, Hideyuki Imai, Ichigaku Takigawa, 2016, ICONIP.

Front Cover: Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data (ChemCatChem 18/2019)

Ichigaku Takigawa, Keisuke Suzuki, Takashi Toyao, 2019 .

Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks

Masahito Kurihara, Ichigaku Takigawa, Satoshi Oyama, 2019, 2019 IEEE International Conference on Big Data (Big Data).

Jointly learning relevant subgraph patterns and nonlinear models of their indicators pdf

Ichigaku Takigawa, Ryo Shirakawa, Yusei Yokoyama, 2018, ArXiv.

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code

Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka, 2016, Briefings Bioinform..

Classification by reflective convex hulls

Atsuyoshi Nakamura, Mineichi Kudo, Ichigaku Takigawa, 2008, 2008 19th International Conference on Pattern Recognition.

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Ichigaku Takigawa, Keisuke Suzuki, Takashi Toyao, 2019, ChemCatChem.

SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining.

Ichigaku Takigawa, Hiroshi Mamitsuka, Sohiya Yotsukura, 2018, Methods in molecular biology.

On the Minimum l1-Norm Signal Recovery in Underdetermined Source Separation

Atsuyoshi Nakamura, Mineichi Kudo, Jun Toyama, 2004, ICA.

An in silico model for interpreting polypharmacology in drug-target networks.

Koji Tsuda, Ichigaku Takigawa, Hiroshi Mamitsuka, 2013, Methods in molecular biology.

Fast algorithms for finding a minimum repetition representation of strings and trees

Atsuyoshi Nakamura, Mineichi Kudo, Tomoya Saito, 2013, Discret. Appl. Math..

FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search

Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Teramoto, 2018, 2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig).

Annotating gene function by combining expression data with a modular gene network

Ichigaku Takigawa, Hiroshi Mamitsuka, Motoki Shiga, 2007, ISMB/ECCB.

Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis

Ichigaku Takigawa, Hiroshi Mamitsuka, Hiroshi Mamitsuka, 2007, Bioinform..

A Probabilistic Model for Clustering Text Documents with Multiple Fields

Ichigaku Takigawa, Hiroshi Mamitsuka, Shanfeng Zhu, 2007, ECIR.

The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets

Atsuyoshi Nakamura, Mineichi Kudo, Ichigaku Takigawa, 2005, MLDM.

Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric

Akira Tanaka, Hideyuki Imai, Mineichi Kudo, 2012, SSPR/SPR.

Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions

Takashi Horiyama, Mutsunori Banbara, Yutaro Yamaguchi, 2022 .

Density Functional Theory Calculations of Oxygen Vacancy Formation and Subsequent Molecular Adsorption on Oxide Surfaces

Yoyo Hinuma, Takashi Kamachi, Ichigaku Takigawa, 2018, The Journal of Physical Chemistry C.

Mining Patterns from Glycan Structures

Minoru Kanehisa, Ichigaku Takigawa, Hiroshi Mamitsuka, 2010 .

Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys

Ichigaku Takigawa, Ken-ichi Shimizu, Takashi Toyao, 2018 .

Machine-learning prediction of the d-band center for metals and bimetals

Koji Tsuda, Ichigaku Takigawa, Ken-ichi Shimizu, 2016 .

ROS-DET: robust detector of switching mechanisms in gene expression

Koji Tsuda, Ichigaku Takigawa, Hiroshi Mamitsuka, 2011, Nucleic acids research.

CaMPDB: a resource for calpain and modulatory proteolysis.

Ichigaku Takigawa, Hiroshi Mamitsuka, David duVerle, 2010, Genome informatics. International Conference on Genome Informatics.

Structual Change Point Detection for Evolutional Networks

Hideyuki Imai, Mineichi Kudo, Ichigaku Takigawa, 2013 .

On the performance of methods for finding a switching mechanism in gene expression.

Koji Tsuda, Ichigaku Takigawa, Hiroshi Mamitsuka, 2010, Genome informatics. International Conference on Genome Informatics.