Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Shape retrieval based on dynamic programming
IEEE Transactions on Image Processing
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning from Relevant Tasks Only
ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning from Multiple Sources
The Journal of Machine Learning Research
A framework for classifier adaptation and its applications in concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
EigenTransfer: a unified framework for transfer learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Boosting expert ensembles for rapid concept recall
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Transfer Learning with Data Edit
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Relaxed Transfer of Different Classes via Spectral Partition
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient Bayesian task-level transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using large-scale web data to facilitate textual query based retrieval of consumer photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Proceedings of the 18th ACM conference on Information and knowledge management
Heterogeneous transfer learning for image clustering via the social web
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Transfer Learning beyond Text Classification
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Learning Algorithms for Domain Adaptation
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Knowledge transferring via implicit link analysis
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Three challenges in data mining
Frontiers of Computer Science in China
Building re-usable dictionary repositories for real-world text mining
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Predictive distribution matching SVM for multi-domain learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Relevant subtask learning by constrained mixture models
Intelligent Data Analysis
Towards semantic knowledge propagation from text corpus to web images
Proceedings of the 20th international conference on World wide web
Improving accuracy of microarray classification by a simple multi-task feature selection filter
International Journal of Data Mining and Bioinformatics
Knowledge transfer based on feature representation mapping for text classification
Expert Systems with Applications: An International Journal
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Transfer learning through domain adaptation
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Adaptive boosting for transfer learning using dynamic updates
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Focused multi-task learning using gaussian processes
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Active class selection for arousal classification
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Inductive transfer learning for handling individual differences in affective computing
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Transfer learning for cross-company software defect prediction
Information and Software Technology
Leveraging Auxiliary Data for Learning to Rank
ACM Transactions on Intelligent Systems and Technology (TIST)
Journal of Biomedical Informatics
Learning from positive and unlabeled examples with different data distributions
ECML'05 Proceedings of the 16th European conference on Machine Learning
Improving bayesian learning using public knowledge
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Rapid pedestrian detection in unseen scenes
Neurocomputing
Sentiment detection with auxiliary data
Information Retrieval
Co-transfer learning via joint transition probability graph based method
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
Transfer learning for pedestrian detection
Neurocomputing
Hi-index | 0.03 |
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second, auxiliary, source of data is available drawn from a different distribution. This auxiliary data is more plentiful, but of significantly lower quality, than the training and test data. In the SVM framework, a training example has two roles: (a) as a data point to constrain the learning process and (b) as a candidate support vector that can form part of the definition of the classifier. The paper considers using the auxiliary data in either (or both) of these roles. This auxiliary data framework is applied to a problem of classifying images of leaves of maple and oak trees using a kernel derived from the shapes of the leaves. Experiments show that when the training data set is very small, training with auxiliary data can produce large improvements in accuracy, even when the auxiliary data is significantly different from the training (and test) data. The paper also introduces techniques for adjusting the kernel scores of the auxiliary data points to make them more comparable to the training data points.