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Multi-label classification tasks such as OCR and multi-object recognition are a major focus of the growing machine learning as a service industry.
The multiple-choice knapsack problem
Sinha, P. and Zoltners, A. A · 1979
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Knapsack problems: Algorithms and computer implementations
Pamela H. Vance, S. M. and Toth), P · 1993
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On quality of service optimization with discrete qos options
Lee, C., Lehoczky, J. P., Rajkumar, R., and Siewiorek, D. P · 1999
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The mosek interior point optimizer for linear programming: an implementation of the homogeneous algorithm
Andersen, E. D. and Andersen, K. D · 2000
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Sang, E. F. T. K. and Meulder, F. D · 2003
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Cbc user guide
Forrest, J. and Lougee-Heimer, R · 2005
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Solving the multidimensional multiple-choice knapsack problem by constructing convex hulls
Akbar, M. M., Rahman, M. S., Kaykobad, M., Manning, E. G., and Shoja, G. C · 2006
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The gurobi optimizer
Bixby, B · 2007
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Random k -labelsets: An ensemble method for multilabel classification
Tsoumakas, G. and Vlahavas, I. P · 2007
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The MIR flickr retrieval evaluation
Huiskes, M. J. and Lew, M. S · 2008
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Multi-label classification using ensembles of pruned sets
Read, J., Pfahringer, B., and Holmes, G · 2008
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Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding
Zhou, Y. and Naroditskiy, V · 2008
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The adwords problem: Online keyword matching with budgeted bidders under random permutations
Devanur, N. R. and Hayes, T. P · 2009
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Classifier chains for multi-label classification
Read, J., Pfahringer, B., Holmes, G., and Frank, E · 2011
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A cascade ranking model for efficient ranked retrieval
Wang, L., Lin, J. J., and Metzler, D · 2011
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Ensemble approach based on conditional random field for multi-label image and video annotation
Xu, X., Jiang, Y., Peng, L., Xue, X., and Zhou, Z · 2011
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The groningen meaning bank
Bos, J · 2013
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Deep convolutional network cascade for facial point detection
Sun, Y., Wang, X., and Tang, X · 2013
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Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K · 2013
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Microsoft COCO: common objects in context
Lin, T., Maire, M., Belongie, S. J., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Classifier cascades and trees for minimizing feature evaluation cost
Xu, Z. E., Kusner, M. J., Weinberger, K. Q., Chen, M., and Chapelle, O · 2014
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A review on multi-label learning algorithms
Zhang, M. and Zhou, Z · 2014
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Learning complexity-aware cascades for deep pedestrian detection
Cai, Z., Saberian, M. J., and Vasconcelos, N · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. M. A., Gool, L. V., Williams, C. K. I., Winn, J. M., and Zisserman, A · 2015
Cited alongside, same era.
Efficient and robust automated machine learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F · 2015
Cited alongside, same era.
Ensemble application of convolutional and recurrent neural networks for multi-label text categorization
Chen, G., Ye, D., Xing, Z., Chen, J., and Cambria, E · 2017
Cited alongside, same era.
Towards a method for automatically selecting and configuring multi-label classification algorithms
de Sá, A. G. C., Pappa, G. L., and Freitas, A. A · 2017
Cited alongside, same era.
Google’s cloud vision API is not robust to noise
ICDAR 2019 competition on large-scale street view text with partial labeling
Sun, Y., Karatzas, D., Chan, C. S., Jin, L., Ni, Z., Chng, C. K., Liu, Y., Luo, C., Ng, C. C., Han, J., Ding, E., and Liu, J · 2019
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ICDAR 2019 robust reading challenge on reading chinese text on signboard
Zhang, R., Yang, M., Bai, X., Shi, B., Karatzas, D., Lu, S., Jawahar, C. V., Zhou, Y., Jiang, Q., Song, Q., Li, N., Zhou, K., Wang, L., Wang, D., and Liao, M · 2019
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https://aws.amazon.com/comprehend
Amazon Comprehend API · 2020
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https://labs.everypixel.com/api
Everypixel Image Tagging API · 2020
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https://cloud.google.com/natural-language
Google NLP API · 2020
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https://cloud.google.com/vision
Google Vision API · 2020
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Hosseini, H., Xiao, B., and Poovendran, R · 2017
Cited alongside, same era.
Ontonotes : A large training corpus for enhanced processing
Weischedel, R., Hovy, E., Marcus, M., and Palmer, M · 2017
Cited alongside, same era.
Complexity vs. performance: empirical analysis of machine learning as a service
Yao, Y., Xiao, Z., Wang, B., Viswanath, B., Zheng, H., and Zhao, B. Y · 2017
Cited alongside, same era.
A multi-criteria approach to approximate solution of multiple-choice knapsack problem
Bednarczuk, E. M., Miroforidis, J., and Pyzel, P · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
Cited alongside, same era.
Focus, segment and erase: An efficient network for multi-label brain tumor segmentation
Chen, X., Liew, J., Xiong, W., Chui, C., and Ong, S. H · 2018
Cited alongside, same era.
Contest on robust reading for multi-type web images
He, M., Liu, Y., Yang, Z., Zhang, S., Luo, C., Gao, F., Zheng, Q., Wang, Y., Zhang, X., and Jin, L · 2018
Cited alongside, same era.
https://www.ibm.com/cloud/watson-natural-language-understanding
IBM NLP API · 2020
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https://global.xfyun.cn/products/wordRecg
iFLYTEK Text Recognition API · 2020
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https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision
Microsoft computer vision API · 2020
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https://github.com/PaddlePaddle/PaddleOCR
PaddleOCR, a text recgonition tool from GitHub · 2020
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https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1
SSD, a multi-label image classification tool from GitHub/TensorflowHub · 2020
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https://github.com/explosion/spaCy
spaCy, a named entity recognition tool from GitHub · 2020
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https://intl.cloud.tencent.com/product/ocr
Tencent Text Recognition API · 2020
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https://github.com/zjy-ucas/ChineseNER/tree/master/data
ZHNER dataset · 2020
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FrugalML: How to use ML prediction apis more accurately and cheaply
Chen, L., Zaharia, M., and Zou, J · 2020
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PP-OCR: A practical ultra lightweight OCR system
Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai, Y., Yu, Z., Yang, Y., Dang, Q., and Wang, H · 2020
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Racial disparities in automated speech recognition
Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., and Goel, S · 2020
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The open images dataset V4
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J. R. R., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., and Ferrari, V · 2020
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Simple and fast algorithm for binary integer and online lp
Li, X., Sun, C., and Ye, Y · 2020
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How did the model change? efficiently assessing machine learning api shifts
Chen, L., Zaharia, M., and Zou, J · 2021
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Automl for multi-label classification: Overview and empirical evaluation
Wever, M., Tornede, A., Mohr, F., and Hüllermeier, E · 2021
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