A Uni ed Learning Framework Multisets Modeling Learning

Abstract A uni ed learning framework is proposed Its di erent special cases will automatically lead us to current existing major types of neural network learnings e g data clustering various PCA type self organizations and their localized extensions self organizing topological map as well as supervised learning for feedforward network and modular architecture Not only this new framework is useful for a deep understanding of the existing learnings but also it provides new insights that can guide the extensions of the existing learning methods With the new insights we have obtained several new intersting unsupervised learnings and introduced regularization or generalization to unsupervised learnings particularly proposed approaches for solving a classical hard problem how to decide the number of clusters in clustering analysis or competitive learning