Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.
Code (2)
Tasks
Transfer LearningSimilar Papers 제목 키워드 기반
A new semi-supervised inductive transfer learning framework: Co-Transfer
In many practical data mining scenarios, such as network intrusion detection, Twitter spam detection, and computer-aided diagnosis, a source domain that is different from but related to a target domain is very common. In…
Intrusion DetectionNetwork Intrusion DetectionSpam detectionTransfer Learning3D Pose Transfer with Correspondence Learning and Mesh Refinement
3D pose transfer is one of the most challenging 3D generation tasks. It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e.g., body shape) of the target mesh. Some previous works require…
3D GenerationPose TransferResidual Feature Integration is Sufficient to Prevent Negative Transfer
Transfer learning typically leverages representations learned from a source domain to improve performance on a target task. A common approach is to extract features from a pre-trained model and directly apply them for ta…
Transfer LearningMulti-modal Instance Refinement for Cross-domain Action Recognition
Unsupervised cross-domain action recognition aims at adapting the model trained on an existing labeled source domain to a new unlabeled target domain. Most existing methods solve the task by directly aligning the feature…
Action RecognitionDomain Adaptationobject-detectionObject Detection+2Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization
Large language models (LLMs) face significant challenges when balancing multiple high-level objectives, such as generating coherent, relevant, and high-quality responses while maintaining efficient task adaptation across…