Metric Learning by Amaury Yang
Introduction.- Metrics.- Properties of Metric Learning Algorithms.- Linear Metric Learning.- Nonlinear and Local Metric Learning.- Metric Learning for Special Settings.- Metric Learning for Structured Data.- Generalization Guarantees for Metric Learning.- Applications.- Conclusion.- Bibliography.- Authors' Biographies .-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Aurélien Bellet received his Ph.D. in Machine Learning from the University of Saint-Etienne (France) in 2012. His work focused on algorithmic and theoretical aspects of metric and similarity learning. After completing his thesis, he was a postdoctoral researcher at the University of Southern California, where he worked on large-scale and distributed machine learning with applications to automatic speech recognition. He is currently a postdoctoral researcher at Telecom ParisTech (France), working on machine learning for big data.Amaury Habrard received a Ph.D. in Machine Learning in 2004 from the University of Saint-Etienne. He was Assistant Professor at the Laboratoire dInformatique Fondamentale of Aix-Marseille University until 2011, where he received a habilitation thesis in 2010. He is currently Professor in the Machine Learning group at the Hubert Curien laboratory of the University of Saint-Etienne. His research interests include metric learning, transfer learning, online learningand learning theory.Marc Sebban received a Ph.D. in Machine Learning in 1996 from the Universite of Lyon 1. After four years spent at the French West Indies and Guyana University as Assistant Professor, he got a position of Professor in 2002 at the University of Saint-Etienne (France). Since 2010, he is the head of the Machine Learning group and the director of the Computer Science, Cryptography and Imaging department of the Hubert Curien laboratory. His research interests focus on ensemble methods, metric learning, transfer learning and more generally on statistical learning theory.
| SKU | Unavailable |
| ISBN 13 | 9783031004445 |
| ISBN 10 | 3031004442 |
| Title | Metric Learning |
| Author | Aurlien Muise |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2015-02-12 |
| Number of pages | 139 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |