Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi
Machine Learning and Knowledge Discovery in Databases Applied Data Science and Demo Track Book PDF

Author : Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi
Publisher : Springer Nature
Page : 429 pages
File Size : 43,5 Mb
Release : 2023-09-16
Category : Computers
ISBN : 9783031434303

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track by Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

Yuxiao Dong,Georgiana Ifrim,Dunja Mladenić,Craig Saunders,Sofie Van Hoecke
Machine Learning and Knowledge Discovery in Databases Applied Data Science and Demo Track Book PDF

Author : Yuxiao Dong,Georgiana Ifrim,Dunja Mladenić,Craig Saunders,Sofie Van Hoecke
Publisher : Springer Nature
Page : 608 pages
File Size : 52,6 Mb
Release : 2021-02-24
Category : Computers
ISBN : 9783030676704

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track by Yuxiao Dong,Georgiana Ifrim,Dunja Mladenić,Craig Saunders,Sofie Van Hoecke Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases: Research Track

Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Machine Learning and Knowledge Discovery in Databases Research Track Book PDF

Author : Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Publisher : Springer Nature
Page : 802 pages
File Size : 53,6 Mb
Release : 2023-09-16
Category : Computers
ISBN : 9783031434129

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Machine Learning and Knowledge Discovery in Databases: Research Track by Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

Yuxiao Dong,Dunja Mladenić,Craig Saunders
Machine Learning and Knowledge Discovery in Databases Applied Data Science Track Book PDF

Author : Yuxiao Dong,Dunja Mladenić,Craig Saunders
Publisher : Springer Nature
Page : 612 pages
File Size : 51,7 Mb
Release : 2021-02-24
Category : Computers
ISBN : 9783030676674

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track by Yuxiao Dong,Dunja Mladenić,Craig Saunders Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases

Yuxiao Dong,Georgiana Ifrim,Dunja Mladenić,Craig Saunders,Sofie Van Hoecke
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Yuxiao Dong,Georgiana Ifrim,Dunja Mladenić,Craig Saunders,Sofie Van Hoecke
Publisher : Unknown
Page : 577 pages
File Size : 40,7 Mb
Release : 2021
Category : Application software
ISBN : 3030676714

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Machine Learning and Knowledge Discovery in Databases by Yuxiao Dong,Georgiana Ifrim,Dunja Mladenić,Craig Saunders,Sofie Van Hoecke Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio- ) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi
Machine Learning and Knowledge Discovery in Databases Applied Data Science and Demo Track Book PDF

Author : Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi
Publisher : Springer
Page : 0 pages
File Size : 53,9 Mb
Release : 2023-09-18
Category : Computers
ISBN : 3031434269

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track by Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases

Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Page : 783 pages
File Size : 54,8 Mb
Release : 2021-02-24
Category : Computers
ISBN : 9783030676643

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Machine Learning and Knowledge Discovery in Databases by Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases

Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Page : 770 pages
File Size : 41,7 Mb
Release : 2021-02-24
Category : Computers
ISBN : 9783030676612

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Machine Learning and Knowledge Discovery in Databases by Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases

Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Page : 797 pages
File Size : 50,6 Mb
Release : 2021-02-24
Category : Computers
ISBN : 9783030676582

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Machine Learning and Knowledge Discovery in Databases by Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases

Ulf Brefeld,Edward Curry,Elizabeth Daly,Brian MacNamee,Alice Marascu,Fabio Pinelli,Michele Berlingerio,Neil Hurley
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Ulf Brefeld,Edward Curry,Elizabeth Daly,Brian MacNamee,Alice Marascu,Fabio Pinelli,Michele Berlingerio,Neil Hurley
Publisher : Springer
Page : 706 pages
File Size : 51,6 Mb
Release : 2019-01-17
Category : Computers
ISBN : 9783030109974

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Machine Learning and Knowledge Discovery in Databases by Ulf Brefeld,Edward Curry,Elizabeth Daly,Brian MacNamee,Alice Marascu,Fabio Pinelli,Michele Berlingerio,Neil Hurley Pdf

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learning; ensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

Machine Learning and Knowledge Discovery in Databases

Yuxiao Dong,Dunja Mladenić,Craig Saunders
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Yuxiao Dong,Dunja Mladenić,Craig Saunders
Publisher : Unknown
Page : 580 pages
File Size : 54,8 Mb
Release : 2021
Category : Application software
ISBN : 3030676684

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Machine Learning and Knowledge Discovery in Databases by Yuxiao Dong,Dunja Mladenić,Craig Saunders Pdf

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio- ) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases: Research Track

Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Machine Learning and Knowledge Discovery in Databases Research Track Book PDF

Author : Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Publisher : Springer Nature
Page : 506 pages
File Size : 50,9 Mb
Release : 2023-09-17
Category : Computers
ISBN : 9783031434242

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Machine Learning and Knowledge Discovery in Databases: Research Track by Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases: Research Track

Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Machine Learning and Knowledge Discovery in Databases Research Track Book PDF

Author : Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Publisher : Springer Nature
Page : 789 pages
File Size : 44,6 Mb
Release : 2023-09-17
Category : Computers
ISBN : 9783031434211

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Machine Learning and Knowledge Discovery in Databases: Research Track by Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning and Knowledge Discovery in Databases: Research Track

Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Machine Learning and Knowledge Discovery in Databases Research Track Book PDF

Author : Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi
Publisher : Springer Nature
Page : 758 pages
File Size : 45,9 Mb
Release : 2023-09-16
Category : Computers
ISBN : 9783031434150

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Machine Learning and Knowledge Discovery in Databases: Research Track by Danai Koutra,Claudia Plant,Manuel Gomez Rodriguez,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.