Simple and Efficient Learning using Privileged Information
By Xinxing Xu, Joey Tianyi Zhou, Ivor Wai-Hung Tsang, Zheng Qin, Rick Siow Mong Goh, Yong Liu. 2016
12 citations recorded by the source corpus.
The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase. In this work, we propose an efficient solution for SVM+ by simply utilizing the squared hinge loss instead of the hinge loss as in the existing SVM+ formulation, which interestingly leads to a dual form with less variables and in the same form with the dual of the standard SVM. The proposed algorithm is utilized to leverage the additional web knowledge that is only available during training for the image categorization tasks. The extensive experimental results on both Caltech101 andWebQueries datasets show that our proposed method can achieve a factor of up to hundred times speedup with the comparable accuracy when compared with the existing SVM+ method.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yong Liu
- Yong Kiam Tan
- Xinxing Xu
- Dietmar Jannach
- Malte Ludewig
- Alexandros Karatzoglou
- Balázs Hidasi
- Aminu Da’u
In Computer Vision and Pattern Recognition
Citations in the corpus
- Improved Recurrent Neural Networks for Session-based Recommendations (2016, methodology): “The learning using privileged information (LUPI) framework [27, 30, 32] was proposed to utilize the additional feature representations that are only available during training but not during testing.”