论文标题
概率分类向量机的转移学习扩展
Transfer learning extensions for the probabilistic classification vector machine
论文作者
论文摘要
转移学习的重点是在新背景下重复使用监督学习模型。可以在机器人技术，图像处理或Web挖掘中找到突出的应用程序。在这些领域，学习场景自然改变了，但通常保持彼此相关，激发了现有监督模型的重复使用。当前的转移学习模型既不稀疏也可以解释。如果必须在技术有限的环境中使用这些方法，并且由于隐私法规而变得越来越重要，那么稀疏性是非常可取的。在这项工作中，我们建议将两个转移学习扩展集成到稀疏且可解释的概率分类向量机器中。将它们与该领域的标准基准进行比较，并通过稀疏性或绩效改进来表明其相关性。
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.
