61 classical papers about transfer leanring

迁移学习经典文献61篇
....................\!@Transferred correlation learning.pdf
....................\@A Comparative Study of Methods for Transductive.pdf
....................\@A NEW TOPIC-BRIDGED MODEL FOR TRANSFER LEARNING.pdf
....................\@A Pedestrian Classification Method Based on.pdf
....................\@A Survey on Transfer Learning.pdf
....................\@A Weighted Algorithm of Inductive Transfer.pdf
....................\@An Active Learning Approach to Knowledge.pdf
....................\@An Efficient Active Learning Algorithm.pdf
....................\@Bayesian Task-Level Transfer Learning for.pdf
....................\@Building topographic subspace model with transfer learning for sparse representation.pdf
....................\@Comparing the Applicability of Two Learning.pdf
....................\@Cross-database age estimation.pdf
....................\@Distributed Transfer Network Learning.pdf
....................\@Empirical Evidence of Priming.pdf
....................\@Extending Semi-supervised Learning Methods for Inductive Transfer Learning.pdf
....................\@Graph-Based Analysis of Human Transfer.pdf
....................\@Integration of Transfer of Learning to the Adaptive.pdf
....................\@Knowledge Transfer and Learning Race in Strategic Alliance.pdf
....................\@Knowledge Transfer and Opinion Detection in the TREC2006 Blog Track.pdf
....................\@Learning the Shared Subspace for Multi-Task Clustering and Transductive Transfer.pdf
....................\@Multi-Label Transfer Learning with Sparse.pdf
....................\@Multi-Task Active Learning with Output Constraints.pdf
....................\@Multi-Task Sparse Discriminant Analysis (MtSDA) with Overlapping Categories.pdf
....................\@Multi-domain spoken language understanding with transfer learning.pdf
....................\@Multiagent Transfer Learning via Assignment-based Decomposition.pdf
....................\@Multimedia learning- Cognitive individual differences and display design techniques predict transfer learning with multimedia learning.pdf
....................\@On universal transfer learning.pdf
....................\@Optimizing Kernel Functions Using Transfer Learning from Unlabeled Data.pdf
....................\@Pen-based Knowledge Transfer Techniques to Support Lifelong.pdf
....................\@Probabilistic Policy Reuse for inter-task transfer learning.pdf
....................\@Research on Localized Learning and Knowledge Transfer.pdf
....................\@Research on the Presentation Mode in E-Learning based on Transfer Learning.pdf
....................\@Self-taught learning transfer learning from unlabeled data.pdf
....................\@Some Applications in Transfer Learning.pdf
....................\@Supervised Self-taught Learning.pdf
....................\@Task Similarity Measures for Transfer in.pdf
....................\@The Impact of Organizational Learning on.pdf
....................\@Towards a Learning Perspective on Knowledge Technology Transfer through.pdf
....................\@Transfer AdaBoost Learning for Action Recognition.pdf
....................\@Transfer Learning based on SVD for Spam Filtering.pdf
....................\@Transfer Learning in Collaborative Filtering for Sparsity Reduction.pdf
....................\@Transfer Learning in Decision Trees.pdf
....................\@Transferred Feature Selection.pdf
....................\@Transferring Localization Models across Space.pdf
....................\Transfer learning for text classification.pdf
....................\Transferring Localization Models over Time.pdf
....................\Web Usage Mining to Evaluate the.pdf
....................\Web Wrapper Generation Using Tree.pdf
....................\迁移学习及应用.pdf
....................\！@Adaptive Localization Through Transfer.pdf
....................\！@Adaptive Transfer Learning.pdf
....................\！@Boosting for Transfer Learning.pdf
....................\！@Boosting for transfer learning with.pdf
....................\！@Genetic transfer learning.pdf
....................

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．