Regular Article: Open problems in “systems that learn”
Proceedings of the 30th IEEE symposium on Foundations of computer science
Overfitting and undercomputing in machine learning
ACM Computing Surveys (CSUR)
Machine Learning
Machine Learning
Incremental learning from positive data
Journal of Computer and System Sciences
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
On the Strength of Incremental Learning
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
LEARN++: an incremental learning algorithm for multilayer perceptron networks
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Efficient Spam Email Filtering using Adaptive Ontology
ITNG '07 Proceedings of the International Conference on Information Technology
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Reducing the effect of out-voting problem in ensemble based incremental support vector machines
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
An Optimal Global Nearest Neighbor Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards a better understanding of incremental learning
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Incremental algorithm driven by error margins
DS'06 Proceedings of the 9th international conference on Discovery Science
Ensemble of SVMs for incremental learning
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning to combine representations for medical records search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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In the past decades, machine learning models, especially supervised learning algorithms, have been widely used in various real world applications. However, no matter how strong a learning model is, it will suffer from the prediction errors when it is applied to real world problems. Due to the black box nature of supervised learning models, it is a challenging problem to fix the supervised learning models by further learning from the failure cases it generates. In this paper, we propose a novel Local Patch Framework (LPF) to locally fix supervised learning models by learning from its predicted failure cases. Since the learning models are generally globally optimized during training process, our proposed LPF assumes that most of the learning errors are led by local errors in the model. Thus we aim to break the black boxes of learning models by identifying and fixing the local errors of various models automatically. The proposed LPF has two key steps, which are local error region subspace learning and local patch model learning. Through this way, we aim to fix the errors of learning models locally and automatically with certain generalization ability on unseen testing data. Experiments on both classification and ranking problems show that the proposed LPF is effective and outperforms the original algorithms and the incremental learning model.