ABSTRACT
This chapter introduces a family of unsupervised algorithms that have a basis in self-organization, yet are somewhat free from many of the constraints typical of other well known self-organizing architectures. Within this family, the basic processing unit is known as the self-organizing tree map (SOTM). The chapter provides an in-depth coverage of this architecture and its derivations. The self-organizing map (SOM) is an unsupervised neural network model that implements a characteristic nonlinear projection from the high dimensional space of input signal onto a low dimensional array of neurons. Relevence feedback (RF) is a popular and effective way to improve the performance of content-based retrieval (CBR). In 1971, the best known and most widely used nonlinear filter, the median filter, was introduced by Tukey as a time series analysis tool for robust noise suppression. The design of the median filter is based on the theory of order statistics, it possesses the characteristic of robust estimation.
