Original Contribution: Stacked generalization
Neural Networks
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Global Training of Document Processing Systems Using Graph Transformer Networks
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose two different ways of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale decompositions of the predicted labels: one using a gaussian function and another using a frequency function. Moreover, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on image classification task. Results on this task clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.