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PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
[article]
2017
arXiv
pre-print
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We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. ...
From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. ...
This work is partially funded by the French ANR project LIVES ANR-15-CE23-0026-03, the "Région Rhône-Alpes", and by the CIFAR program in Learning in Machines & Brains. ...
arXiv:1606.07240v3
fatcat:r6ljlwcbynatffoipbbetax73m
PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach
[chapter]
2017
Lecture Notes in Computer Science
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We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. ...
From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. ...
This work is partially funded by the French ANR project LIVES ANR-15-CE23-0026-03, the "Région Rhône-Alpes", and by the CIFAR program in Learning in Machines & Brains. ...
doi:10.1007/978-3-319-71246-8_13
fatcat:ygt7fkveubbu5kg2cf5pk6ib6a
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
2019
Neurocomputing
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We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. ...
In this paper we propose a boosting based multiview learning algorithm, referred as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information, and ii ...
Conclusion In this paper, we provide a PAC-Bayesian analysis for a two-level hierarchical multiview learning approach with more than two views, when the model takes the form of a weighted majority vote ...
doi:10.1016/j.neucom.2019.04.072
fatcat:n4ymmppwm5canagzeeo33rmibu
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
[article]
2018
arXiv
pre-print
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We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. ...
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ...
Conclusion In this paper, we provide a PAC-Bayesian analysis for a two-level hierarchical multiview learning approach with more than two views, when the model takes the form of a weighted majority vote ...
arXiv:1808.05784v2
fatcat:pdtsg4zgmbazxf4d6gxf7aq6oi
A survey of multi-view machine learning
2013
Neural computing & applications (Print)
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This paper reviews theories developed to understand the properties and behaviors of multi-view learning, and gives a taxonomy of approaches according to the machine learning mechanisms involved and the ...
Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. ...
generalization error analysis for other multi-view learning approaches. ...
doi:10.1007/s00521-013-1362-6
fatcat:kzt7hibfo5axheedlaofw3pb7m
Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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For example, when shopping for shoes online, consumers usually look through their images, ratings, and product's reviews before making their decisions. ...
To learn multi-aspect factors, many context-aware models have been developed based on tensor factorizations. ...
Conclusion In this paper, we offer a novel hierarchical Bayesian model to find multi-view outliers under a semi-supervised detection scenario via inductive learning. ...
doi:10.24963/ijcai.2020/335
dblp:conf/ijcai/WangL20
fatcat:re35ls7675bqdo5efyld3uofse
Sentiment Analysis
[chapter]
2017
Encyclopedia of Machine Learning and Data Mining
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multiview learning. ...
Multiview Approaches Multiview learners are a class of algorithms for domains in which the features can be partitioned in disjoint subsets (views), each of which is sufficient to learn the target concept ...
It might then be possible to learn, for example, that taking action action234 in state state42 is worth 6. 2 and leads to state state654321. ...
doi:10.1007/978-1-4899-7687-1_100512
fatcat:ce4yyqo2czftzcx2kbauglh3fu
Spike-Timing-Dependent Plasticity
[chapter]
2017
Encyclopedia of Machine Learning and Data Mining
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multiview learning. ...
Multiview Approaches Multiview learners are a class of algorithms for domains in which the features can be partitioned in disjoint subsets (views), each of which is sufficient to learn the target concept ...
It might then be possible to learn, for example, that taking action action234 in state state42 is worth 6. 2 and leads to state state654321. ...
doi:10.1007/978-1-4899-7687-1_774
fatcat:2jprihjaxfbtpb3ttwuuz3u34y
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction
[article]
2014
arXiv
pre-print
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Applications for these approaches already include tutoring systems, autonomous grasping learning, navigation and mapping and human-robot interaction. ...
In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks. ...
For a further analysis an more recent algorithm see the discussion in (Strehl and Littman, 2008) . ...
arXiv:1403.1497v1
fatcat:aryy42dvqzhw3mcsnl4xm5pyhy
Message from the general chair
2015
2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
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To maximize the utility of the injected knowledge, we deploy a learning-based multi-sieve approach and develop novel entity-based features. ...
Joint Learning for Coreference Resolution with Markov Logic
Resolving "This-issue" Anaphora Varada Kolhatkar and Graeme Hirst Saturday 12:00pm-12:30pm -202 A (ICC) We annotate and resolve a particular ...
We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. ...
doi:10.1109/ispass.2015.7095776
dblp:conf/ispass/Lee15
fatcat:ehbed6nl6barfgs6pzwcvwxria
Conformal predictions in multimedia pattern recognition
2011
ACM SIGMultimedia Records
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Existing approaches that provide a measure of confidence on a prediction such as learning algorithms based on the Bayesian theory or the Probably Approximately Correct theory require strong assumptions ...
(iii) Development of a methodology to extend the CP framework to continuous learning, by using the framework for online active learning. ...
The two major theories are: Bayesian Learning and Probably Approximately Correct (PAC) Learning, each of which is discussed below. ...
doi:10.1145/2069196.2069202
fatcat:pptvfth4bffxtllc3m665qodze
Deep learning in medical imaging and radiation therapy
2018
Medical Physics (Lancaster)
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The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies ...
dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions. ...
, many DL systems used multiview 2D CNNs for analysis of CT and MRI datasets in what is referred to as 2.5D analysis. ...
doi:10.1002/mp.13264
pmid:30367497
fatcat:bottst5mvrbkfedbuocbrstcnm
A survey on data‐efficient algorithms in big data era
2021
Journal of Big Data
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AbstractThe leading approaches in Machine Learning are notoriously data-hungry. ...
This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with ...
The hierarchical nature of representations in DNN makes them a viable candidate for semi-supervised approaches. ...
doi:10.1186/s40537-021-00419-9
fatcat:v4uahsvhlzdldlxqf24bshmja4
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
[article]
2019
arXiv
pre-print
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This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look ...
This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. ...
Canonical Correlation Analysis (CCA) [5] is a standard approach to learning two linear projections of two sets of data that are maximally correlated. ...
arXiv:1705.04396v3
fatcat:iknfmppi5zca7ljovdlwvdwluu
Neural Networks Regularization Through Representation Learning
[article]
2018
arXiv
pre-print
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Many approaches have been proposed to prevent the network from overfitting and improve its generalization performance such as data augmentation, early stopping, parameters sharing, unsupervised learning ...
In this thesis, we tackle the neural network overfitting issue from a representation learning perspective by considering the situation where few training samples are available which is the case of many ...
I would like to thank as well my collaborators Acknowledgements Precioso at University of Nice-Sophia Antipolis for inviting me to give a talk in his deep learning summer school. ...
arXiv:1807.05292v1
fatcat:qwqvdyzkf5alrjtp6fnclxpuqu
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