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Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature…
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Related topics
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Constrained conditional model
Deep feature synthesis
Deep learning
Feature (machine learning)
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Broader (1)
Machine learning
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts
Nicolay Rusnachenko
,
Natalia V. Loukachevitch
2019
Corpus ID: 132673663
In this paper we present an application of the specific neural network model for sentiment attitude extraction without…
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2018
2018
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning
N. Hartmann
,
L. B. D. Santos
BEA@NAACL-HLT
2018
Corpus ID: 46940692
This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature…
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2018
2018
Classification of Hanging Garments Using Learned Features Extracted from 3D Point Clouds
Jan Stria
,
Václav Hlaváč
IEEE/RJS International Conference on Intelligent…
2018
Corpus ID: 52227265
The presented work deals with classification of garment categories including pants, shorts, shirts, T-shirts and towels. The…
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2017
2017
VeNICE: A very deep neural network approach to no-reference image assessment
P. Dash
,
A. Wong
,
A. Mishra
International Conference on Industrial Technology
2017
Corpus ID: 19692872
Image Quality Assessment (IQA) remains a complex and challenging problem that has garnered great interest by the research…
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2017
2017
Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks
Eduardo Fonseca
,
Rong Gong
,
D. Bogdanov
,
Olga Slizovskaia
,
E. Gómez
,
Xavier Serra
Workshop on Detection and Classification of…
2017
Corpus ID: 44972262
Comunicacio presentada al Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), celebrat el dia…
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2016
2016
Deep learning based human behavior recognition in industrial workflows
Konstantinos Makantasis
,
A. Doulamis
,
N. Doulamis
,
Konstantinos Psychas
International Conference on Information Photonics
2016
Corpus ID: 16983288
We consider the fully automated behavior understanding through visual cues in industrial environments. In contrast to most…
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2016
2016
Offline Arabic Handwritten recognition system with dropout applied in Deep networks based-SVMs
M. Elleuch
,
Raouia Mokni
,
M. Kherallah
IEEE International Joint Conference on Neural…
2016
Corpus ID: 8260739
As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many…
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2016
2016
Towards automated satellite image segmentation and classification for assessing disaster damage using data-specific features with incremental learning
A. Vetrivel
,
N. Kerle
,
M. Gerke
,
F. Nex
,
G. Vosselman
2016
Corpus ID: 36704794
Automated damage assessment based on satellite imagery is crucial for initiating fast response actions. Several methods based on…
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2015
2015
A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
Chenxi Zhu
,
Xipeng Qiu
,
Xinchi Chen
,
Xuanjing Huang
Annual Meeting of the Association for…
2015
Corpus ID: 8755918
In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense…
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Highly Cited
2011
Highly Cited
2011
Shallow Discourse Parsing with Conditional Random Fields
Sucheta Ghosh
,
Richard Johansson
,
G. Riccardi
,
Sara Tonelli
International Joint Conference on Natural…
2011
Corpus ID: 9713133
Parsing discourse is a challenging natural language processing task. In this paper we take a data driven approach to identify…
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