Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A fast learning algorithm for deep belief nets
Neural Computation
Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Deep Architectures for AI
Learning Deep Architectures for AI
PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
ACM Transactions on Intelligent Systems and Technology (TIST)
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Semiconducting bilinear deep learning for incomplete image recognition
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Does adding more training data always help improve the effectiveness of a machine-learning or pattern-recognition task? Recent evidences in machine translation and speech recognition seem to suggest that the data-driven approach outperforms the traditional model-based approach. Instead of carefully modeling rules and their exceptions, the data-driven approach relies on identifying similar patterns in massive datasets and then uses the similar patterns to predict the labels (or other outcomes) of unseen instances. In this work, we compare representative data-driven and model-based schemes on an image annotation task. We enumerate pros and cons of these two approaches, and propose a hybrid approach, which can harness the strengths of the two.