Discovery Series Class Set
Download Discovery Series Class Set full books in PDF, epub, and Kindle. Read online free Discovery Series Class Set ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Dimitrios Gunopulos |
| Publisher | : Springer |
| Total Pages | : 702 |
| Release | : 2011-09-06 |
| Genre | : Computers |
| ISBN | : 3642237835 |
This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.
| Author | : Gianmarco De Francisci Morales |
| Publisher | : Springer Nature |
| Total Pages | : 429 |
| Release | : 2023-09-16 |
| Genre | : Computers |
| ISBN | : 3031434307 |
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.
| Author | : United States. Securities and Exchange Commission |
| Publisher | : |
| Total Pages | : 1568 |
| Release | : 2002 |
| Genre | : Securities |
| ISBN | : |
| Author | : Jesús Ariel Carrasco-Ochoa |
| Publisher | : Springer |
| Total Pages | : 399 |
| Release | : 2013-06-05 |
| Genre | : Computers |
| ISBN | : 3642389899 |
This book constitutes the refereed proceedings of the 5th Mexican Conference on Pattern Recognition, MCPR 2013, held in Huatulco, Mexico, in June 2013. The 36 revised full papers and two keynotes presented were carefully reviewed and selected from 81 submissions and are organized in topical sections on computer vision; image processing; pattern recognition and artificial intelligence; neural networks; document processing.
| Author | : Michael J Palmer |
| Publisher | : Royal Society of Chemistry |
| Total Pages | : 459 |
| Release | : 2011-10-28 |
| Genre | : Science |
| ISBN | : 184973349X |
There are about 8 million deaths each year from neglected tropical diseases (NTDs) in the underdeveloped world, whilst drug discovery focus and practice is only recently taking on greater urgency and embracing the latest technologies. This unique book is a state of the art review of drug discovery in respect of NTDs and highlights best practice to guide the ongoing drug discovery effort and also to raise debate and awareness in areas that remain highly neglected. All the major diseases such as malaria, trypanosomatids and TB are covered, with a review of each disease and established compounds, new mechanistic classes and new horizons. Each chapter highlights the key science that has led to breakthroughs, with detailed assessment of the key medicinal chemistry involved, and critical appraisal of new emerging approaches. Later chapters highlight under publicized disease areas where the medical needs are neglected and research is very limited, to raise awareness. The editors, acknowledged experts in the field, have a wealth of experience in successful drug discovery practice and tropical diseases.
| Author | : Françoise Fogelman-Soulié |
| Publisher | : IOS Press |
| Total Pages | : 388 |
| Release | : 2008 |
| Genre | : Computers |
| ISBN | : 1586038982 |
The real power for security applications will come from the synergy of academic and commercial research focusing on the specific issue of security. This book is suitable for those interested in understanding the techniques for handling very large data sets and how to apply them in conjunction for solving security issues.
| Author | : Frank Hutter |
| Publisher | : Springer Nature |
| Total Pages | : 770 |
| Release | : 2021-02-24 |
| Genre | : Computers |
| ISBN | : 3030676617 |
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.
| Author | : Joanne C. Dauer |
| Publisher | : Heritage Capital Corporation |
| Total Pages | : 442 |
| Release | : 2002 |
| Genre | : Paper money |
| ISBN | : 9780972846608 |
| Author | : Stephen Neidle |
| Publisher | : Academic Press |
| Total Pages | : 673 |
| Release | : 2013-09-30 |
| Genre | : Medical |
| ISBN | : 0123972280 |
Cancer Drug Design and Discovery, Second Edition is an important reference on the underlying principles for the design and subsequent development of new anticancer small molecule agents. New chapters have been added to this edition on areas of particular interest and therapeutic promise, including cancer genomics and personalized medicine, DNA-targeted agents and more. This book includes several sections on the basic and applied science of cancer drug discovery and features those drugs that are now approved for human use and are in the marketplace, as well as those that are still under development. By highlighting some of the general principles involved in taking molecules through basic science to clinical development, this book offers a complete and authoritative reference on the design and discovery of anticancer drugs for translational scientists and clinicians involved in cancer research. - Provides a clinical perspective on the development of new molecularly targeted anticancer agents with the latest and most promising chemotherapeutic approaches - Offers a broad view of where the field is going, what tools drug discovery is using to produce new agents and how they are evaluated in the laboratory and clinic - Features 6 new chapters devoted to advances in technology and successful anticancer therapies, such as cancer genomics and personalized medicine, DNA-targeted agents, B-Raf inhibitors and more - Each chapter includes extensive references to the primary and review literature, as well as to relevant web-based sources
| Author | : Alberto Fernández |
| Publisher | : Springer |
| Total Pages | : 385 |
| Release | : 2018-10-22 |
| Genre | : Computers |
| ISBN | : 3319980742 |
This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.
