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Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case [article]

Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh, Mike A. Nalls, Saurabh Sinha, Roy H. Campbell
2017 arXiv   pre-print
These top data mining and machine learning algorithms cover classification, clustering, regression, graphical model-based learning, and dimensionality reduction.  ...  In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms  ...  Acknowledgment This research program is sponsored in part through RDA/US Data Share grant from the Alfred P.  ... 
arXiv:1710.00112v1 fatcat:d6wr6wzxdbfvhn4zwbyrvlvax4

Deep Learning and Data Mining Classification through the Intelligent Agent Reasoning

Amine Chemchem, Francois Alin, Michael Krajecki
2018 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)  
Over the last few years, machine learning and data mining methods (MLDM) are constantly evolving, in order to accelerate the process of knowledge discovery from data (KDD).  ...  Index Terms-data mining, machine learning, intelligent agent, knowledge mining.  ...  ACKNOWLEDGMENT The authors would like to thank Julien.L, Arnaud.R & Fabien.B: our colleagues and administrators of the supercomputer DGX 1 in ROMEO HPC Center 6 for their helps and suggestions.  ... 
doi:10.1109/w-ficloud.2018.00009 dblp:conf/ficloud/ChemchemAK18 fatcat:ruzm6og42bfyrkvqyg43fmo55y

Distributed GraphLab: A Framework for Machine Learning in the Cloud [article]

Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein
2012 arXiv   pre-print
mining and machine learning algorithms and can lead to inefficient learning systems.  ...  In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees.  ...  Joseph Gonzalez is supported by a Graduate Research Fellowship from the National Science Foundation and a fellowship from AT&T Labs.  ... 
arXiv:1204.6078v1 fatcat:g3cnoya2qbc7hpeqv4ttmua74e

International congress news

2005 Journal of Information Processing and Management  
◆7月9日~11日 MLDM 2005: IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition 主 催:Institute of Computer Vision and an Applied mail:  ...  EISTA 2005: 3rd International Conferense on Education and Information Systems, technologies and applications 主 催:International Institute of Informatics and Systemics (IIIS) 場 所:Sheraton World Resort,  ... 
doi:10.1241/johokanri.48.193 fatcat:lzotmduf5ffrxbneogqvuka4o4

Distributed GraphLab

Yucheng Low, Danny Bickson, Joseph Gonzalez, Carlos Guestrin, Aapo Kyrola, Joseph M. Hellerstein
2012 Proceedings of the VLDB Endowment  
mining and machine learning algorithms and can lead to inefficient learning systems.  ...  In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees.  ...  Joseph Gonzalez is supported by a Graduate Research Fellowship from the National Science Foundation and a fellowship from AT&T Labs.  ... 
doi:10.14778/2212351.2212354 fatcat:tfgpcqbyajb2dof7kslqct5ebu

International congress news

2003 Journal of Information Processing and Management  
International conference on machine learning and data mining in pattern recognition.  ...  主 催:International Association for Pattern Recognition, Thames Ditton (UK) 場 所:Leipzig (Germany) 問合せ:Institut fuer Bildverarbeitung und angewandte Informatik Dr.  ...  IFLA 2003: 69 . annual council and general conference of the International Federation of Library Association and Institutions (IFLA): Access point library -Media, information, culture.  ... 
doi:10.1241/johokanri.46.201 fatcat:pf3mquzjrvfdzewzcblr6gxhrm

Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2009-2013) [article]

Alberto Martin-Martin, Enrique Ordunna-Malea, Juan Manuel Ayllon, Emilio Delgado Lopez-Cozar
2015 arXiv   pre-print
Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2009-2013).  ...  The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered  ...  which Emilio Delgado López-Cózar is the principal investigator, and project APOSTD/2013/002 from the Regional Ministry of Education, Culture and Sports  ... 
arXiv:1412.7633v2 fatcat:m6dtylc6qradff3h3xwzhyfuti

Statistical computation methods for microbiome compositional data network inference [article]

Liang Chen, Qiuyan He, Hui Wan, Shun He, Minghua Deng
2021 arXiv   pre-print
On the one hand, features of microbiome data such as compositionality, sparsity and high-dimensionality challenge the data normalization and the design of computational methods.  ...  Their scope of applications, advantages and limitations are presented in this review.  ...  For shotgun data where the taxonomic level can be the species level, [66] utilizes both the microbial co-occurrence in literature and the machine learning method proposed in [70] to classify abstracts  ... 
arXiv:2109.01993v1 fatcat:lpg2kcil55d43ch2ottgbqspne

Transforming paper documents into XML format with WISDOM++

Oronzo Altamura, Floriana Esposito, Donato Malerba
2001 International Journal on Document Analysis and Recognition  
We are also grateful to Lynn Rudd for her help in re-reading the paper.  ...  The authors would like to thank Francesco De Tommaso, Dario Gerbino, Ignazio Sardella, Giacomo Sidella, Rosa Maria Spadavecchia, and Silvana Spagnoletta for their contribution to the development of WISDOM  ...  machine learning tools.  ... 
doi:10.1007/pl00013569 fatcat:x7sr5ak32nc27pbf4bbzq4c57a

Discovery of the Latent Supportive Relevance in Medical Data Mining [chapter]

Sam Chao, Fai Wong, Qiulin Ding, Yiping Li, Mingchui Dong
2008 Data Mining in Medical and Biological Research  
FS and CFD have been the active and fruitful fields of research for decades in statistics, pattern recognition, machine learning and data mining (Yu & Liu, 2004) .  ...  Decision tree is such a method that most widely used and practical for inductive inference in the data mining and machine learning discipline (Han & Kamber, 2000) .  ...  Publisher InTech Published online 01, November, 2008 Published in print edition November, 2008 This book intends to bring together the most recent advances and applications of data mining research  ... 
doi:10.5772/6404 fatcat:wh3tw2klzzestdokd7x5nmq7ku

Top-down induction of model trees with regression and splitting nodes

D. Malerba, F. Esposito, M. Ceci, A. Appice
2004 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Model trees induced by SMOTI are generally simple and easily interpretable and their analysis often reveals interesting patterns.  ...  Experimental results on artificially generated data sets show that SMOTI outperforms two model tree induction systems, M5' and RETIS, in accuracy.  ...  on data mining, machine learning, and document processing.  ... 
doi:10.1109/tpami.2004.1273937 pmid:15460282 fatcat:5i3gr7b5bbh6zi4tdknan5yehq

A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association [chapter]

Ahmed Aljuboori, Farid Meziane, David Parsons
2016 Lecture Notes in Computer Science  
KDD process involves many phases i.e. data pre-processing, data integration, data transformation, data mining, pattern recognition and knowledge representation.  ...  As is the case in many fields, in data mining and machine learning, a key part of any study is how the system is evaluated.  ... 
doi:10.1007/978-3-319-41920-6_24 fatcat:ca5g5a3j6zaildvyfprv22mj5i

Online clustering via finite mixtures of Dirichlet and minimum message length

Nizar Bouguila, Djemel Ziou
2006 Engineering applications of artificial intelligence  
Mixture modeling is the problem of identifying and modeling components in a given set of data.  ...  The online algorithm is based on unsupervised learning of finite Dirichlet mixtures and a stochastic approach for estimates updating.  ...  Acknowledgements The completion of this research was made possible, thanks to the Natural Sciences and Engineering Research Council of Canada, Heritage Canada and Bell Canada's support through its Bell  ... 
doi:10.1016/j.engappai.2006.01.012 fatcat:3h5zbnjtirgdpjocie445nygva

Learning Negative Mixture Models by Tensor Decompositions [article]

Guillaume Rabusseau, François Denis
2014 arXiv   pre-print
(i) We show that every rational probability distributions on strings, a representation which occurs naturally in spectral learning, can be computed by a negative mixture of at most two probabilistic automata  ...  Building upon a recent paper on tensor decompositions for learning latent variable models, we extend this work to the broader setting of tensors having a symmetric decomposition with positive and negative  ...  Acknowledgments This work has been carried out thanks to the support of the ARCHIMEDE Labex (ANR-11-LABX-0033) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the "Investissements d'Avenir" French  ... 
arXiv:1403.4224v2 fatcat:bmdoo5b5rfchzdlahtnsogxrc4

Implementing decision tree-based algorithms in medical diagnostic decision support systems

Mohammad M. Ghiasi
This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database.  ...  As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized  ...  models/algorithms, machine learning, and data mining approaches.  ... 
doi:10.48336/exjj-3c11 fatcat:rppacflnondvnjho6utl3lszka
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