Lazy Transfer Learning

Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception

This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative perception of largescale environmental phenomena. To overcome the limitations of scale in existing works, our proposed algorithms allow every mobile sensing agent to choose a different support set and dynamically switch to another during...

Lazy Learners at work : the Lazy Learning

A Lazy Ensemble Learning Method to Classification

Depending on how a learner reacts to the test instances, supervised learning divided into eager learning and lazy learning. Lazy learners endeavor to find local optimal solutions for each particular test instance. Many approaches for constructing lazy learning have been developed, one of the successful one is to incorporate lazy learning with ensemble classification. Almost all lazy learning sc...

Learning radial basis neural networks in a lazy way: A comparative study

Lazy learning methods have been used to deal with problems in which the learning examples are not

A Case-Based Reasoning View of Automated Collaborative Filtering

From some perspectives Automated Collaborative Filtering (ACF) appears quite similar to Case-Based Reasoning (CBR). It works on data organised around users and assets that might be considered case descriptions. In addition, in some versions of ACF, much of the induction is deferred to run time – in the lazy learning spirit of CBR. On the other hand, because of its lack of semantic descriptions ...