Галерея 3014697

Галерея 3014697
All Books Conferences Courses Journals & Magazines Standards Authors Citations
Abstract: One of the main challenges in multi-source domain adaptation is how to reduce the domain discrepancy between each source domain and a target domain, and then evaluate the... View more
One of the main challenges in multi-source domain adaptation is how to reduce the domain discrepancy between each source domain and a target domain, and then evaluate the domain relevance to determine how much knowledge should be transferred from different source domains to the target domain. However, most prior approaches barely consider both discrepancies and relevance among domains. In this paper, we propose an algorithm, called Iterative Refinement based on Feature Selection and the Wasserstein distance (IRFSW), to solve semi-supervised domain adaptation with multiple sources. Specifically, IRFSW aims to explore both the discrepancies and relevance among domains in an iterative learning procedure, which gradually refines the learning performance until the algorithm stops. In each iteration, for each source domain and the target domain, we develop a sparse model to select features in which the domain discrepancy and training loss are reduced simultaneously. Then a classifier is constructed with the selected features of the source and labeled target data. After that, we exploit optimal transport over the selected features to calculate the transferred weights. The weight values are taken as the ensemble weights to combine the learned classifiers to control the amount of knowledge transferred from source domains to the target domain. Experimental results validate the effectiveness of the proposed method.
Date of Publication: 06 August 2020
References is not available for this document.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060

Contact & Support

Domain adaptation aims to leverage the knowledge in one or multiple source domains to assist the prediction in a target domain, where the source and target domains have different data distributions. Domain adaptation has shown promising performance in handling the situations where training and test data follow different distributions [1]. It has been successfully applied in many real-world applications, such as text classification [2], [3], [4], sentiment analysis [5], [6], [7], visual recognition [8], [9], [10], [11], [12], and so on. According to the availability of labeled target data, we divide domain adaptation into two categories: unsupervised domain adaptation and semi-supervised domain adaptation. In the unsupervised setting, no labeled target data are available during the training procedure [8], [13], [14]. While in the semi-supervised setting, a few labeled target data are provided in advance for training a learning model [9], [10], [11]. In this paper, we consider the setting of semi-supervised domain adaptation with multiple source domains.
Proceedings of 1994 IEEE International Symposium on Information Theory
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Alle Titel TV-Folgen Prominente Unternehmen Stichwörter Erweiterte Suche
Vollständig unterstützt English (United States) Teilweise unterstützt Français (Canada) Français (France) Deutsch (Deutschland) हिंदी (भारत) Italiano (Italia) Português (Brasil) Español (España) Español (México)
Leah Kilpatrick Besetzung Produktion Produktionsleitung
Leah Kilpatrick wurde in Fort Meyers, Florida, USA geboren. Sie ist Schauspielerin und Produzentin, bekannt für Babysplitters (2019) , Super-Fan Builds (2016) und Best Man Dead Man .
Zusätzliche Crew 3 Drehbuch 2 Abteilung Ausstattung 1 Location Management 1 Als er/sie selbst 2
Act Super Naturally Carrie Ann Turner WNRD News Field Reporter
Sunny Family Cult Stacey Fernsehserie
Impressions Inc. Real Mary Kurzfilm
Crazy, Stupid, Love Girl at Bar (Nicht genannt)
Free the Nipple linienproduzent co-producer
Garden of Echoes assoziierter produzent Kurzfilm
Gateway produzent (produced by) Kurzfilm
Lenny Morris vs. The Universe assoziierter produzent Kurzfilm
The Wardrobe produktionsleiter Kurzfilm
Delicious produktionsleiter Kurzfilm
The Falling of the Bright unit-produktionsleiter Kurzfilm
La llorona unit-produktionsleiter Kurzfilm
Bearbeitung vorschlagen oder fehlenden Inhalt hinzufügen
Sie haben keine kürzlich angezeigten Seiten.
Melde dich an für Zugriff auf mehr Inhalte Melde dich an für Zugriff auf mehr Inhalte

Finnish regulators are voting on a fairer copyright law. That’s amazing enough. More amazing still: The law was proposed by the general population.

Be in the Know. Subscribe to Fast Company Newsletters.
An award-winning team of journalists, designers, and videographers who tell brand stories through Fast Company's distinctive lens
The future of innovation and technology in government for the greater good
Fast Company's annual ranking of businesses that are making an outsize impact
Leaders who are shaping the future of business in creative ways
New workplaces, new food sources, new medicine--even an entirely new economic system
Celebrating the best ideas in business
Talk about crowdsourcing: Finland is set to vote on a set of copyright laws that weren’t proposed by government or content-making agencies: They were drafted by citizens.
Finns are able to propose laws that the government must consider if 50,000 supporters sign a petition calling for the law within six months. A set of copyright regulations that are fairer to everyone just passed that threshold, and TorrentFreak.com reports that a government vote is likely in early 2014. The new laws were created with the help of the Finnish Electronic Frontier Foundation, and the body has promised that it will maintain pressure on the political system so that the law will actually be changed.
The proposed new laws would decriminalize file sharing and prevent house searches and surveillance of pirates. TorrentFreak reminds us of the international media outcry that happened last year when during a police raid a 9-year-old girl’s laptop was confiscated on the grounds that she stole copyrighted content. Finland’s existing copyright laws, under what’s called the Lex Karpela amendment , are very strict and criminalize the breaking of DRM for copying purposes as well as preventing discussion of the technology for doing so. The laws have been criticized by activists and observers for their strictness and infringement upon freedom of speech.
The crowdsourcing of ideas for legislation is a growing trend. Congresswoman Zoe Lofgren asked Reddit users for ideas to curb the draconian process of seizing domain names in the U.S. Steven Polunsky, director of the Texas Senate Committee on Business and Commerce, proposed a crowdsourcing effort for new legislation regulating payday lending. In Iceland in 2011 the nation as a whole helped crowdsource a new constitution .
I'm covering the science/tech/generally-exciting-and-innovative beat for Fast Company. Follow me on Twitter , or Google+ and you'll hear tons of interesting stuff, I promise.

I've also got a PhD, and worked in such roles as professional scientist and theater technician...thankfully avoiding jobs like bodyguard and chicken shed-cleaner (bonus points if you get that reference!)

Super-Fan Builds with Man at Arms: Reforged

Стройная жена сняв черное бикини показала черный лобок
Сексуальные фото блондинки из Польши (не голая)
Неверная жена часто зовет любовника из негритянского гетто к себе домой

Галерея 3014697

Report Page