Transfer learning extensions for the probabilistic classification vector machine

TL;DR

This paper introduces two transfer learning extensions for the probabilistic classification vector machine that enhance sparsity and interpretability, outperforming standard benchmarks in relevant scenarios.

Contribution

It presents novel transfer learning methods integrated into a sparse, interpretable probabilistic classifier, addressing limitations of existing models.

Findings

Extensions improve sparsity and interpretability.

Methods outperform benchmarks in relevant tasks.

Relevance shown through performance and sparsity improvements.

Abstract

Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning models are neither sparse nor interpretable. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we propose two transfer learning extensions integrated into the sparse and interpretable probabilistic classification vector machine. They are compared to standard benchmarks in the field and show their relevance either by sparsity or performance improvements.