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Taking into Account the Differences between Actively and Passively
  Acquired Data: The Case of Active Learning with Support Vector Machines for
  Imbalanced Datasets

Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets

North American Chapter of the Association for Computational Linguistics (NAACL), 2009
17 September 2014
Michael Bloodgood
K. Vijay-Shanker
ArXiv (abs)PDFHTML

Papers citing "Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets"

16 / 16 papers shown
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic
  Segmentation
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationIEEE International Conference on Robotics and Automation (ICRA), 2023
Jiarong Wei
Yancong Lin
Holger Caesar
429
8
0
12 Oct 2023
A Survey of Active Learning for Natural Language Processing
A Survey of Active Learning for Natural Language ProcessingConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Zhisong Zhang
Emma Strubell
Eduard H. Hovy
LM&MA
356
82
0
18 Oct 2022
Impact of Stop Sets on Stopping Active Learning for Text Classification
Impact of Stop Sets on Stopping Active Learning for Text ClassificationInternational Computer Science Conference (ICSC), 2022
Luke Kurlandski
Michael Bloodgood
278
3
0
08 Jan 2022
Unsupervised Instance Selection with Low-Label, Supervised Learning for
  Outlier Detection
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection
Trent J. Bradberry
Christopher H. Hase
LeAnna Kent
Joel A. Góngora
166
1
0
26 Apr 2021
A Survey of Deep Active Learning
A Survey of Deep Active LearningACM Computing Surveys (ACM CSUR), 2020
Pengzhen Ren
Yun Xiao
Xiaojun Chang
Po-Yao (Bernie) Huang
Zhihui Li
Brij B. Gupta
Xiaojiang Chen
Xin Wang
544
1,435
0
30 Aug 2020
Discovery of Self-Assembling $π$-Conjugated Peptides by Active
  Learning-Directed Coarse-Grained Molecular Simulation
Discovery of Self-Assembling πππ-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationJournal of Physical Chemistry B (J. Phys. Chem. B), 2020
Kirill Shmilovich
R. Mansbach
Hythem Sidky
Olivia E. Dunne
S. Panda
J. Tovar
Andrew L. Ferguson
264
85
0
27 Jan 2020
Early Forecasting of Text Classification Accuracy and F-Measure with
  Active Learning
Early Forecasting of Text Classification Accuracy and F-Measure with Active LearningInternational Computer Science Conference (ICSC), 2020
T. Orth
Michael Bloodgood
AI4TS
109
2
0
20 Jan 2020
Corpus Wide Argument Mining -- a Working Solution
Corpus Wide Argument Mining -- a Working SolutionAAAI Conference on Artificial Intelligence (AAAI), 2019
L. Ein-Dor
Eyal Shnarch
Lena Dankin
Alon Halfon
Benjamin Sznajder
...
Leshem Choshen
Yufang Hou
Yonatan Bilu
R. Aharonov
Noam Slonim
214
69
0
25 Nov 2019
The Use of Unlabeled Data versus Labeled Data for Stopping Active
  Learning for Text Classification
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
Garrett Beatty
Ethan Kochis
Michael Bloodgood
91
20
0
26 Jan 2019
Impact of Batch Size on Stopping Active Learning for Text Classification
Impact of Batch Size on Stopping Active Learning for Text Classification
Garrett Beatty
Ethan Kochis
Michael Bloodgood
104
15
0
24 Jan 2018
Support Vector Machine Active Learning Algorithms with
  Query-by-Committee versus Closest-to-Hyperplane Selection
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection
Michael Bloodgood
113
24
0
24 Jan 2018
Filtering Tweets for Social Unrest
Filtering Tweets for Social UnrestInternational Computer Science Conference (ICSC), 2017
Alan Mishler
Kevin Wonus
Wendy Chambers
Michael Bloodgood
121
12
0
20 Feb 2017
Analysis of Stopping Active Learning based on Stabilizing Predictions
Analysis of Stopping Active Learning based on Stabilizing Predictions
Michael Bloodgood
John Grothendieck
113
22
0
23 Apr 2015
Bucking the Trend: Large-Scale Cost-Focused Active Learning for
  Statistical Machine Translation
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Michael Bloodgood
Chris Callison-Burch
153
73
0
21 Oct 2014
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Michael Bloodgood
Chris Callison-Burch
114
49
0
20 Oct 2014
A Method for Stopping Active Learning Based on Stabilizing Predictions
  and the Need for User-Adjustable Stopping
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable StoppingConference on Computational Natural Language Learning (CoNLL), 2009
Michael Bloodgood
K. Vijay-Shanker
226
102
0
17 Sep 2014
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