Topographic Map Formation By Maximizing Unconditional Entropy : Aplausible Strategy for “Online” Unsupervised Competitive Learning and Nonparametric Density Estimation  

Oleh:

Van Hulle, M. M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 5 (1996), page 1299-1304.
Topik: CURRENT DENSITY; topographic; map formation; entropy; aplausible strategy; learning; non parametric; density estimation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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An unsupervised competitive learning rule, called the vectorial boundary adaptation rule (VBAR), is introduced for topographic map formation. Since VBAR is aimed at producing an equiprobable quantization of the input space, it yields a nonparametric model of the input probability density function. Furthermore, since equiprobable quantization is equivalent to unconditional entropy maximization, we argue that this is a plausible strategy for maximizing mutual information (Shannon information rate) in the case of “online” learning. We use mutual information as a tool for comparing the performance of our rule with Kohonen's self - organizing (feature) map algorithm.

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