Machine Learning With Noisy Labels
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Machine Learning With Noisy Labels
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Author : Gustavo Carneiro
language : en
Publisher: Elsevier
Release Date : 2024-03-01
Machine Learning With Noisy Labels written by Gustavo Carneiro and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-01 with Computers categories.
Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels. Machine learning and Noisy Labels: Definitions, Theory, Techniques and Solutions defines different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods developed in the field. This book is an ideal introduction to machine learning with noisy labels suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching into, machine learning methods. Shows how to design and reproduce regression, classification and segmentation models using large-scale noisy-label training sets Gives an understanding of the theory of, and motivation for, noisy-label learning Shows how to classify noisy-label learning methods into a set of core techniques
Machine Learning Methods With Noisy Incomplete Or Small Datasets
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Author : Jordi Solé-Casals
language : en
Publisher:
Release Date : 2021
Machine Learning Methods With Noisy Incomplete Or Small Datasets written by Jordi Solé-Casals and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.
Learning From Imperfect Data
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Author : Vasilis Kontonis
language : en
Publisher:
Release Date : 2023
Learning From Imperfect Data written by Vasilis Kontonis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
The datasets used in machine learning and statistics are \emph{huge} and often \emph{imperfect},\textit{e.g.}, they contain corrupted data, examples with wrong labels, or hidden biases. Most existing approaches (i) produce unreliable results when the datasets are corrupted, (ii) are computationally inefficient, or (iii) come without any theoretical/provable performance guarantees. In this thesis, we \emph{design learning algorithms} that are \textbf{computationally efficient} and at the same time \textbf{provably reliable}, even when used on imperfect datasets. We first focus on supervised learning settings with noisy labels. We present efficient and optimal learners under the semi-random noise models of Massart and Tsybakov -- where the true label of each example is flipped with probability at most 50\% -- and an efficient approximate learner under adversarial label noise -- where a small but arbitrary fraction of labels is flipped -- under structured feature distributions. Apart from classification, we extend our results to noisy label-ranking. In truncated statistics, the learner does not observe a representative set of samples from the whole population, but only truncated samples, \textit{i.e.}, samples from a potentially small subset of the support of the population distribution. We give the first efficient algorithms for learning Gaussian distributions with unknown truncation sets and initiate the study of non-parametric truncated statistics. Closely related to truncation is \emph{data coarsening}, where instead of observing the class of an example, the learner receives a set of potential classes, one of which is guaranteed to be the correct class. We initiate the theoretical study of the problem, and present the first efficient learning algorithms for learning from coarse data.
Deep Learning Based Multi Modal Perception And Semi Automatic Labelling Algorithms For Automotive Sensor Data
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Author : Christian Haase-Schütz
language : en
Publisher: BoD – Books on Demand
Release Date : 2023
Deep Learning Based Multi Modal Perception And Semi Automatic Labelling Algorithms For Automotive Sensor Data written by Christian Haase-Schütz and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Test Collection Based Evaluation Of Information Retrieval Systems
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Author : Mark Sanderson
language : en
Publisher: Now Publishers Inc
Release Date : 2010-06-03
Test Collection Based Evaluation Of Information Retrieval Systems written by Mark Sanderson and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-03 with Computers categories.
Use of test collections and evaluation measures to assess the effectiveness of information retrieval systems has its origins in work dating back to the early 1950s. Across the nearly 60 years since that work started, use of test collections is a de facto standard of evaluation. This monograph surveys the research conducted and explains the methods and measures devised for evaluation of retrieval systems, including a detailed look at the use of statistical significance testing in retrieval experimentation. This monograph reviews more recent examinations of the validity of the test collection approach and evaluation measures as well as outlining trends in current research exploiting query logs and live labs. At its core, the modern-day test collection is little different from the structures that the pioneering researchers in the 1950s and 1960s conceived of. This tutorial and review shows that despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research.
Multi Label Dimensionality Reduction
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Author : Liang Sun
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Multi Label Dimensionality Reduction written by Liang Sun and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Business & Economics categories.
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
Artificial Neural Networks And Machine Learning Icann 2022
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Author : Elias Pimenidis
language : en
Publisher: Springer Nature
Release Date : 2022-09-06
Artificial Neural Networks And Machine Learning Icann 2022 written by Elias Pimenidis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-06 with Computers categories.
The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications. Chapter “Sim-to-Real Neural Learning with Domain Randomisation for Humanoid Robot Grasping ” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Machine Learning And Knowledge Discovery In Databases Research Track
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Author : Danai Koutra
language : en
Publisher: Springer Nature
Release Date : 2023-09-16
Machine Learning And Knowledge Discovery In Databases Research Track written by Danai Koutra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-16 with Computers categories.
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.
Artificial Neural Networks And Machine Learning Icann 2017
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Author : Alessandra Lintas
language : en
Publisher: Springer
Release Date : 2017-10-24
Artificial Neural Networks And Machine Learning Icann 2017 written by Alessandra Lintas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-24 with Computers categories.
The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning. There are 63 short paper abstracts that are included in the back matter of the volume.
Computer Vision Eccv 2022
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Author : Shai Avidan
language : en
Publisher: Springer Nature
Release Date : 2022-10-20
Computer Vision Eccv 2022 written by Shai Avidan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Computers categories.
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.