Titlebook: Metric Learning; Aurélien Bellet,Amaury Habrard,Marc Sebban Book 2015 Springer Nature Switzerland AG 2015

Daidzein

書目名稱Metric Learning影響因子(影響力)




書目名稱Metric Learning影響因子(影響力)學科排名




書目名稱Metric Learning網(wǎng)絡(luò)公開度




書目名稱Metric Learning網(wǎng)絡(luò)公開度學科排名




書目名稱Metric Learning被引頻次




書目名稱Metric Learning被引頻次學科排名




書目名稱Metric Learning年度引用




書目名稱Metric Learning年度引用學科排名




書目名稱Metric Learning讀者反饋




書目名稱Metric Learning讀者反饋學科排名

locus-ceruleus

978-3-031-00444-5Springer Nature Switzerland AG 2015

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Metric Learning978-3-031-01572-4Series ISSN 1939-4608 Series E-ISSN 1939-4616

Antigen

Metrics,This chapter introduces some background knowledge on metrics and their applications. Section 2.1 provides definitions for distance, similarity and kernel functions. Some standard metrics are presented in Section 2.2. We conclude this chapter by briefly discussing the use of metrics in machine learning and data mining in Section 2.3.

漸強

obsolete

浸軟

Aurélien Bellet,Amaury Habrard,Marc Sebbaneitung.Includes supplementary material: .Dieses Buch bietet eine grundlegende Einführung in das Rechnungswesen für Kulturbetriebe. Für Kulturinstitutionen wird der Umgang mit dem betrieblichen Rechnungswesen aus mehreren Gründen wichtiger: für eine solide Datenbasis, um staatliche F?rderungen zu erh

整潔

Introduction,or conceptual representations. Essentially, when facing stimuli or situations similar to what we have encountered before, we expect similar responses and take similar actions. This has led psychologists to develop a variety of cognitive theories and mathematical models of similarity [Ashby and Perri

OVERT

Properties of Metric Learning Algorithms,scalability, optimality guarantees and ability to perform dimensionality reduction (Figure 3.1). When deciding which method to apply, emphasis should be placed on these properties, depending on the characteristics of the problem at hand. They provide the basis for a taxonomy of all the algorithms co

Cirrhosis

Linear Metric Learning,ient learning thanks to their simple form. The chapter is organized as follows. In the first part, we focus on Malahanobis distance learning (Section 4.1), where the learned metric satisfies the distance axioms. Then, motivated by psychological evidence and computational benefits, the second part is