K-nearest neighbors algorithm

Results: 173



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31Despite the popularity and superior performance of Gaussian-kernel support vector machine (SVM), the two hyperparameters sigma (scale) and C (tradeoff) remain hard to be tuned. Many techniques have been developed to addr

Despite the popularity and superior performance of Gaussian-kernel support vector machine (SVM), the two hyperparameters sigma (scale) and C (tradeoff) remain hard to be tuned. Many techniques have been developed to addr

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Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
32Batch-Incremental vs. Instance-Incremental Learning in Dynamic and Evolving Data Jesse Read1 , Albert Bifet2 , Bernhard Pfahringer2 , Geoff Holmes2 1 Department  of Signal Theory and Communications

Batch-Incremental vs. Instance-Incremental Learning in Dynamic and Evolving Data Jesse Read1 , Albert Bifet2 , Bernhard Pfahringer2 , Geoff Holmes2 1 Department of Signal Theory and Communications

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Source URL: users.ics.aalto.fi

Language: English - Date: 2012-10-25 03:54:39
33FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION Marius Muja, David G. Lowe Computer Science Department, University of British Columbia, Vancouver, B.C., Canada ,

FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION Marius Muja, David G. Lowe Computer Science Department, University of British Columbia, Vancouver, B.C., Canada ,

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Source URL: www.geog.ucsb.edu

Language: English - Date: 2013-05-14 18:54:15
34Deep Learning in Partially-labeled Data Streams Jesse Read Aalto University and HIIT Helsinki, Finland  Fernando Perez-Cruz

Deep Learning in Partially-labeled Data Streams Jesse Read Aalto University and HIIT Helsinki, Finland Fernando Perez-Cruz

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Source URL: users.ics.aalto.fi

Language: English - Date: 2014-12-09 08:08:51
35Multi-Class Object Localization by Combining Local Contextual Interactions Carolina Galleguillos† Brian McFee† Serge Belongie† Gert Lanckriet‡ † Computer Science and Engineering Department

Multi-Class Object Localization by Combining Local Contextual Interactions Carolina Galleguillos† Brian McFee† Serge Belongie† Gert Lanckriet‡ † Computer Science and Engineering Department

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Source URL: eceweb.ucsd.edu

Language: English - Date: 2015-07-31 19:00:26
36artificial-build-D-SMO.eps

artificial-build-D-SMO.eps

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Source URL: users.ics.aalto.fi

Language: English - Date: 2011-05-27 05:37:42
37ICB  ICB CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.  000

ICB ICB CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. 000

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Source URL: www.vast.uccs.edu

Language: English - Date: 2012-07-06 12:49:54
38Machine learning techniques available in pRoloc Laurent Gatto∗ June 14, 2016 Contents 1 Introduction

Machine learning techniques available in pRoloc Laurent Gatto∗ June 14, 2016 Contents 1 Introduction

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Source URL: www.bioconductor.org

Language: English - Date: 2016-06-14 23:40:44
39Two Ellipse-based Pruning Methods for Group Nearest Neighbor Queries Hongga Li School of Computing National University of Singapore, Singapore

Two Ellipse-based Pruning Methods for Group Nearest Neighbor Queries Hongga Li School of Computing National University of Singapore, Singapore

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Source URL: people.cs.aau.dk

Language: English - Date: 2007-06-07 03:45:31
40AttentiveLearner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking Phuong Pham and Jingtao Wang() Computer Science and LRDC, University of Pittsburgh, Pittsburgh, PA, USA {phuongpham,jingtaow}@cs.pitt.e

AttentiveLearner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking Phuong Pham and Jingtao Wang() Computer Science and LRDC, University of Pittsburgh, Pittsburgh, PA, USA {phuongpham,jingtaow}@cs.pitt.e

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Source URL: people.cs.pitt.edu

Language: English - Date: 2015-08-19 10:50:54