367 results for machine learning
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Apprentissage Machine: de la théorie à la pratique
Cet ouvrage présente les fondements scientifiques de la théorie de l'apprentissage supervisé, les algorithmes les plus répandus développés suivant ce domaine ainsi que les deux cadres de l'apprentissage semi-supervisé et de l'ordonnancement, à un niveau accessible aux étudiants de master et aux…
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The Turing_Sahbani Machine
…physical bounds, or the convergence protocol. ABSTRACT The Turing–Sahbani Machine (TSM): A Physically Constrained Theory of Computation, Model Uniqueness, and Set-Theoretic Truth Classification: Mathematical Logic, Set Theory (ZFC_X), Computational Complexity, Quantum Information Theory…
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Recent Advances in Reinforcement Learning
This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008. The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to…
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Interaction Homme-machine pour la formation et l'apprentissage humain
International audience
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of…
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Multi-faceted Deep Learning
This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble…
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Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
…and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys…
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Learning with Partially Labeled and Interdependent Data
This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data.The book traces how the…
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Statistical Learning Tools for Electricity Load Forecasting
This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the…
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Hybrid System Identification: Theory and Algorithms for Learning Switching Models
…It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and…
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Learning with Submodular Functions: A Convex Optimization Perspective
Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the lovasz extension of submodular functions provides a useful set of regularization functions for supervised and…
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. Apprentissage artificiel - 3e édition: Deep Learning, Concepts et algorithmes
International audience
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Nonlinear Control of Uncertain Systems: Conventional and Learning-Based Alternatives with Python
…some of which prove to be conventional and others to employ advanced learning-based methods. This book shows how each category applies to specific groups of problems, but the choice is made based on pragmatic assessments of efficiency and efficacy rather than on dogmatic adherence to the benefits…
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How We Learn: Why Brains Learn Better Than Any Machine... For Now
In our technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever before. In this book, the author decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He…
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Apprentissage artificiel - 4e édition: Concepts et algorithmes - De Bayes et Hume au Deep Learning
Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolutifs (analyse de marchés financiers, diagnostics…
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Deep Learning in Mining of Visual Content
This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. It contains chapters…
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Deep Learning to See: Towards New Foundations of Computer Vision
International audience