'Label set' presentation slideshows

Relaxations and Moves for MAP Estimation in MRFs

S T A N F O R D. Relaxations and Moves for MAP Estimation in MRFs. M. Pawan Kumar. Vladimir Kolmogorov. Philip Torr. Daphne Koller. Our Problem. 0. 6. 1. 3. 2. 0. 4. Label l 2. 1. 2. 4. 1. 1. 3. Label l 1. 1. 0. 5. 0. 3. 7. 2. v 1. v 2. v 3. v 4.

737 views • 63 slides

The Minimum Label Spanning Tree Problem: Some Genetic Algorithm Approaches

The Minimum Label Spanning Tree Problem: Some Genetic Algorithm Approaches. Yupei Xiong, Univ. of Maryland Bruce Golden, Univ. of Maryland Edward Wasil, American Univ. Presented at the Lunteren Conference on the Mathematics of Operations Research The Netherlands, January 2006.

478 views • 38 slides

330 views • 21 slides

An Ensemble-based Approach to Fast Classification of Multi-label Data Streams

An Ensemble-based Approach to Fast Classification of Multi-label Data Streams. Xiangnan Kong, Philip S. Yu. Dept. of Computer Science University of Illinois at Chicago. Introduction: Data Stream. Data Stream: high speed data flow continuously arriving, changing. Applications:

367 views • 20 slides

Composite Labels In Flexi-Grid

Composite Labels In Flexi-Grid. Adrian Farrel Old Dog Consulting adrian@olddog.co.uk. Why Composite Labels?. Composite labels are a way to encode information about multiple quantities that are switched together and carry the same data flow Examples Waveband TDM VCAT OTN VCAT.

243 views • 15 slides

Composite Labels In Flexi-Grid

Composite Labels In Flexi-Grid. Adrian Farrel Old Dog Consulting adrian@olddog.co.uk. Why Composite Labels?. Composite labels are a way to encode information about multiple quantities that are switched together and carry the same data flow Examples Waveband TDM VCAT OTN VCAT.

226 views • 15 slides

Label Embedding Trees for Large Multi-class Tasks

Label Embedding Trees for Large Multi-class Tasks. Samy Bengio Jason Weston David Grangier. Presented by Zhengming Xing. Outline. Introduction Label Trees Label Embeddings Experiment result. Introduction. Large scale problem: the number of example Feature dimension Number of class.

310 views • 14 slides

Robust supervised image classifiers by spatial AdaBoost based on robust loss functions

Robust supervised image classifiers by spatial AdaBoost based on robust loss functions. Ryuei Nishii and Shinto Eguchi Proc. Of SPIE Vol. 5982 59820D-2. terminology. Supervised image classification A prior knowledge of your image area is required. Loss function

264 views • 13 slides

Ethernet TSPEC and MEF Parameters

Ethernet TSPEC and MEF Parameters. draft-ietf-mef-ethernet-traffic-parameters-01.txt dimitri.papadimitriou@alcatel.be. Clarification Points. Point 1: Port vs Frame mode Point 2: VLAN Label. Port vs Frame mode (1). Switching Type (ST) in Label Request object = interface switching

206 views • 11 slides

Sugang Xu, Hiroaki Harai, Daniel King

Extensions to GMPLS RSVP-TE for Bidirectional Lightpath with the Same Wavelength draft-xu-rsvpte-bidir-wave-01. Sugang Xu, Hiroaki Harai, Daniel King xsg@nict.go.jp , harai@nict.go.jp , daniel.king@aria-networks.com. Optical End Node. Optical End Node. Core Node. Core Node. MC. GE. MC.

168 views • 7 slides

Sugang Xu, Hiroaki Harai, Daniel King

Extensions to GMPLS RSVP-TE for Bidirectional Lightpath with the Same Wavelength draft-xu-rsvpte-bidir-wave-00. Sugang Xu, Hiroaki Harai, Daniel King xsg@nict.go.jp , harai@nict.go.jp , daniel.king@aria-networks.com. Optical End Node. Optical End Node. Core Node. Core Node. MC. GE. MC.

167 views • 7 slides

Sugang Xu, Hiroaki Harai, Daniel King

Extensions to GMPLS RSVP-TE for Bidirectional Lightpath with the Same Wavelength draft-xu-rsvpte-bidir-wave-01. Sugang Xu, Hiroaki Harai, Daniel King xsg@nict.go.jp , harai@nict.go.jp , daniel.king@aria-networks.com. Optical End Node. Optical End Node. Core Node. Core Node. MC. GE. MC.

90 views • 7 slides