An evolving classification cascade with self-learning
An evolving classification cascade with self-learning
复制标题
具有自学习能力的不断发展的分类级联
DOI：
10.1007/s12530-010-9014-x
复制
发表时间：
2010
期刊：
影响因子：
3.2
通讯作者：
A. Bouchachia
中科院分区：
文献类型：
--
作者：
A. Bouchachia
增量学习主要是通过依赖于适当的和可调整的架构的增量模型来完成的。本文介绍了一种混合进化体系结构，用于处理增量学习。由两个顺序和增量学习模块：增长高斯混合模型（GGMM）和资源分配神经网络（RAN），该架构的基本原理取决于两个问题：增量和部分标记的数据处理的可能性在分类的上下文中。这两个模块在都依赖于高斯函数的意义上是一致的。虽然由扩展卡尔曼滤波器训练的RAN用于预测，但GGMM专用于使用概率框架对未标记数据进行自学习或预标记。此外，一个增量的特征选择过程中，不断选择有意义的功能。实证评估的级联研究的各个方面，以讨论建议的混合学习架构的效率。
Incremental learning is mostly accomplished by an incremental model relying on appropriate and adjustable architecture. The present paper introduces a hybrid evolving architecture for dealing with incremental learning. Consisting of two sequential and incremental learning modules: growing Gaussian mixture model (GGMM) and resource allocating neural network (RAN), the rationale of the architecture rests on two issues: incrementality and the possibility of partially labeled data processing in the context of classification. The two modules are coherent in the sense that both rely on Gaussian functions. While RAN trained by the extended Kalman filter is used for prediction, GGMM is dedicated to self-learning or pre-labeling of unlabeled data using a probabilistic framework. In addition, an incremental feature selection procedure is applied for continuously choosing the meaningful features. The empirical evaluation of the cascade studies various aspects in order to discuss the efficiency of the proposed hybrid learning architecture.