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1st international workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th international Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Kim, Juae Kwon, Sunjae Ko, Youngjoong Seo, Jungyun Computer Science Sogang University Sinsu-dong 1 Mapo-gu Seoul Korea Republic of Computer Engineering Dong-A University 840 Hadan 2-dong Saha-gu Busan Korea Republic of

ISBN: (纸本)9781948087070

Biomedical Named Entity (NE) recognition is a core technique for various works in the biomedical domain. In previous studies, using machine learning algorithm shows better performance than dictionary-based and rule based approaches because there are too many terminological variations of biomedical NEs and new biomedical NEs are constantly generated. To achieve the high performance with a machine-learning algorithm, good-quality corpora are required. However, it is difficult to obtain the good-quality corpora because annotating a biomedical corpus for machine-learning is extremely time-consuming and costly. In addition, most previous corpora are insufficient for high-level tasks because they cannot cover various domains. Therefore, we propose a method for generating a large amount of machine-labeled data that covers various domains. To generate a large amount of machine-labeled data, firstly we generate an initial machine-labeled data by using a chunker and MetaMap. The chunker is developed to extract only biomedical NEs with manually annotated data. MetaMap is used to annotate the category of biomedical NE. Then we apply the self-training approach to bootstrap the performance of initial machine-labeled data. In our experiments, the biomedical NE recognition system that is trained with our proposed machine-labeled data achieves much high performance. As a result, our system outperforms biomedical NE recognition system that using MetaMap only with 26.03%p improvements on F1-score. © 2017 AFNLP

关键词： learning algorithms

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Speeding up Heterogeneous Federated learning with Sequentially Trained Superclients 26

Speeding up Heterogeneous Federated Learning with Sequential...

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26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Zaccone, Riccardo Rizzardi, Andrea Caldarola, Debora Ciccone, Marco Caputo, Barbara Politecnico Torino Turin Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, Le. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.(1)

关键词： Training Performance evaluation Federated learning Computational modeling Neural networks statistical distributions data models

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1st international workshop on data Analysis patterns in Software Engineering (DAPSE 2013)

1st International Workshop on Data Analysis Patterns in Soft...

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35th international Conference on Software Engineering (ICSE)

作者： Bird, Christian Menzies, Tim Zimmermann, Thomas Microsoft Res Redmond WA 98052 USA West Virginia Univ Virginia CA USA

ISBN: (纸本)9781467330763

data scientists in software engineering seek insight in data collected from software projects to improve software development. The demand for data scientists with domain knowledge in software development is growing rapidly and there is already a shortage of such data scientists. data science is a skilled art with a steep learning curve. To shorten that learning curve, this workshop will collect best practices in form of data analysis patterns, that is, analyses of data that leads to meaningful conclusions and can be reused for comparable data. In the workshop we compiled a catalog of such patterns that will help experienced data scientists to better communicate about data analysis. The workshop was targeted at experienced data scientists and researchers and anyone interested in how to analyze data correctly and efficiently in a community accepted way.

关键词： data science software engineering data mining predictive analytics business intelligence machine learning big data smart data software analytics software intelligence

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XI-ML 2020 - Proceedings of the 1st international workshop on Explainable and Interpretable machine learning, co-located with the 43rd German Conference on Artificial Intelligence, KI 2020

XI-ML 2020 - Proceedings of the 1st International Workshop o...

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1st international workshop on Explainable and Interpretable machine learning, XI-ML 2020

The proceedings contain 7 papers. The topics discussed include: how to teach a computer to learn about microbes: KG-COVID-19 and microbial graph learning;explaining multivariate time series forecasts: an application to predicting the Swedish GDP;towards participatory design spaces for explainable ai interfaces in expert domains;teaching AI to explain its decisions can affect class balance;foundations for solving classification problems with quantitative abstract argumentation;sequential exceptional pattern discovery using pattern-growth: an extensible framework for interpretable machine learning on sequential data;and a comparative study of explainer modules applied to automated skin lesion classification.

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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1st workshop on machine learning and data mining for Sports Analytics, MLSA 2013

The proceedings contain 9 papers. The topics discussed include: why do sports officials dropout?;strategic patterns discovery in RTS-games for e-sport with sequential pattern mining;maps for reasoning in ultimate;predicting the NFL using Twitter;use of performance metrics to forecast success in the national hockey league;finding similar movements in positional data streams;comparison of machine learning methods for predicting the recovery time of professional football players after an undiagnosed injury;predicting NCAAB match outcomes using ML techniques – some results and lessons learned;and key point selection and clustering of swimmer coordination through sparse Fisher-EM.

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Proceedings of the IWK 2010 workshops: international workshop on Design, Evaluation and Refinement of Intelligent Systems, DERIS 2010 and the 1st international workshop on Evolution Support for Model-Based Development and Testing, EMDT 2010 - Part of the Umbrella Conference 55th international Scientific Colloquium, IWK 2010

Proceedings of the IWK 2010 Workshops: International Worksho...

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IWK 2010 workshops: international workshop on DERIS 2010 and the 1st international workshop on Evolution Support for Model-Based Development and Testing, EMDT 2010 - Part of the Umbrella Conference 55th international Scientific Colloquium, IWK 2010

The proceedings contain 12 papers. The topics discussed include: validation of mixed-structured data using pattern mining and information extraction;validation of knowledge-based systems through CommonKADS;composing tactical agents through contextual storyboards;an adaptable e-learning system for pupils with specific learning difficulties;decision-maker-aware design of descriptive data mining;validation of a data mining method for optimal university curricula;model-based software development - perspectives and challenges;a test case generation technique and process;from natural language requirements to a conceptual model;test case reduction methods by using CBR;and evolution support for model-based development and testing - summary.

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Automatic annotation of protein functional class from sparse and imbalanced data sets

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1st international workshop on data mining and Bioinformatics

作者： Jung, Jaehee Thon, Michael R. Texas A&M Univ Dept Comp Sci College Stn TX 77843 USA Texas A&M Univ Dept Plant Pathol & Microbiol College Stn TX 77843 USA

ISBN: (纸本)9783540689706

In recent years, high-throughput genome sequencing and sequence analysis technologies have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common controlled vocabulary for describing gene function however the process for annotating proteins with GO terms is usually through a tedious manual curation process by trained professional annotators. With the wealth of genomic data that are now available, there is a need for accurate automated annotation methods. In this paper, we propose a method for automatically predicting GO terms for proteins by applying statistical pattern recognition techniques. We employ protein functional domains as features and learn independent Support Vector machine classifiers for each GO term. This approach creates sparse data sets with highly imbalanced class distribution. We show that these problems can be overcome with standard feature and instance selection methods. We also present a meta-learning scheme that utilizes multiple SVMs trained for each GO term, resulting in improved overall performance than either SVM can achieve alone. The implementation of the tool is available at http://***/AAPFC.

关键词： gene annotation feature selection gene ontology InterPro imbalanced data

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On the use of Kinect depth data for identity, gender and ethnicity classification from facial images

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pattern recognition LETTERS 2015年第Part2期68卷 270-277页

作者： Boutellaa, Elhocine Hadid, Abdenour Bengherabi, Messaoud Ait-Aoudia, Samy Ctr Dev Technol Avancees Algiers Algeria Ecole Natl Super Informat Algiers Algeria Univ Oulu Ctr Machine Vis Res SF-90100 Oulu Finland

This article explores the usefulness of the depth images provided by the current Microsoft Kinect sensors in different face analysis tasks including identity, gender and ethnicity. Four local feature extraction methods (LBP, LPQ, HOG and BSIF) are investigated for both face texture and shape description. Extensive experiments on three publicly available Kinect face databases are reported. The experimental analysis yields into interesting findings. Furthermore, a comprehensive review of the literature on the use of Kinect depth data in face analysis is provided along with the description of the available databases. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Face analysis Depth images Microsoft Kinect Soft biometrics

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Proceedings of MLSDA 2013 - The 1st workshop on machine learning for Sensory data Analysis

Proceedings of MLSDA 2013 - The 1st Workshop on Machine Lear...

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1st workshop on machine learning for Sensory data Analysis, MLSDA 2013

ISBN: (纸本)9781450323697

The proceedings contain 8 papers. The topics discussed include: a hybrid grid-based method for mining arbitrary regions-of-interest from trajectories;ensemble feature ranking for shellfish farm closure cause identification;clustering household electricity use profiles;predicting petroleum reservoir properties from downhole sensor data using an ensemble model of neural networks;light-weight online predictive data aggregation for wireless sensor networks;and performance analysis of duty-cycling wireless sensor networks for train localization.

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Quantum computing based machine learning method and its application in radar emitter signal recognition 1

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1st international Conference on Modeling Decisions for Artificial Intelligence

作者： Zhang, GX Hu, LZ Jin, WD Natl EW Lab Chengdu 610036 Sichuan Peoples R China SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

ISBN: (纸本)3540225552

Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is proposed to select the optimal feature subset from a large number of features extracted from radar emitter signals (RESs). The criterion function is given firstly. Then, detailed algorithm of QGA is described and its performances are analyzed. Finally, the best feature subset is selected from the original feature set (OFS) composed of 16 features of RESs. Experimental results show that the proposed approach reduces greatly the dimensions of OFS and heightens accurate recognition rate of RESs, which indicates that NFSA is feasible and effective.

关键词： learning systems

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