Sciweavers

    843 search results - page 11 / 169
    » Learning aspect models with partially labeled data
    Sort
    View
    ECCV
    2008
    Springer
    171views Computer Vision» more  ECCV 2008»
    Learning to Recognize Activities from the Wrong View Point
    14 years 9 months ago
    Learning to Recognize Activities from the Wrong View Point
    Download  www.cs.uiuc.edu
    Appearance features are good at discriminating activities in a fixed view, but behave poorly when aspect is changed. We describe a method to build features that are highly stable u...
    Ali Farhadi, Mostafa Kamali Tabrizi
    claim paper
    Read More »
    NAACL
    2003
    152views Computational Linguistics» more  NAACL 2003»
    Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
    13 years 8 months ago
    Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
    Download  acl.ldc.upenn.edu
    This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
    Shona Douglas
    claim paper
    Read More »
    CVPR
    2008
    IEEE
    233views Computer Vision» more  CVPR 2008»
    Latent topic random fields: Learning using a taxonomy of labels
    14 years 9 months ago
    Latent topic random fields: Learning using a taxonomy of labels
    Download  www.cs.toronto.edu
    An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn...
    Xuming He, Richard S. Zemel
    claim paper
    Read More »
    SDM
    2004
    SIAM
    142views Data Mining» more  SDM 2004»
    Learning to Read Between the Lines: The Aspect Bernoulli Model
    13 years 8 months ago
    Learning to Read Between the Lines: The Aspect Bernoulli Model
    Download  www.cis.hut.fi
    We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
    Ata Kabán, Ella Bingham, T. Hirsimäki
    claim paper
    Read More »
    IJCAI
    2003
    87views Artificial Intelligence» more  IJCAI 2003»
    A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
    13 years 8 months ago
    A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
    Download  www.ri.cmu.edu
    This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11b network. Our approach uses data labeled by ground trut...
    Sebastian Thrun, Geoffrey J. Gordon, Frank Pfennin...
    claim paper
    Read More »
    Explore & Download
    Proceedings Preprints Top 5 Ranked Papers Publications Books Software Tutorials Presentations Lectures Notes Datasets
    Productivity Tools
    International On-screen Keyboard Graphical Social Symbols OCR Text Recognition CSS3 Style Generator Web Page to PDF Web Page to Image PDF Split PDF Merge Latex Equation Editor Sci2ools
    Document Tools
    PDF to Text PDF to Postscript PDF to Thumbnails Excel to PDF Word to PDF Postscript to PDF PowerPoint to PDF Latex to Word Repair Corrupted PDF
    Image Tools
    JPG to PS JPG to PDF Extract Images from PDF Image Converter
    Sciweavers
    About Community Cookies Privacy Policy Terms of Use

    Copyright © Sciweavers LLC, 2009-2024, USA.