@inproceedings{64c7bbfb13ac4ca19b94e442b10fb518,
title = "Fast and balanced: Efficient label tree learning for large scale object recognition",
abstract = "We present a novel approach to efficiently learn a label tree for large scale classification with many classes. The key contribution of the approach is a technique to simultaneously determine the structure of the tree and learn the classifiers for each node in the tree. This approach also allows fine grained control over the efficiency vs accuracy trade-off in designing a label tree, leading to more balanced trees. Experiments are performed on large scale image classification with 10184 classes and 9 million images. We demonstrate significant improvements in test accuracy and efficiency with less training time and more balanced trees compared to the previous state of the art by Bengio et al.",
author = "Jia Deng and Sanjeev Satheesh and Berg, {Alexander C.} and Li, {Fei Fei}",
year = "2011",
language = "English (US)",
isbn = "9781618395993",
series = "Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011",
publisher = "Neural Information Processing Systems",
booktitle = "Advances in Neural Information Processing Systems 24",
note = "25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 ; Conference date: 12-12-2011 Through 14-12-2011",
}