Integrative Self-Organizing Map—A Mean Pattern Model
نویسندگان
چکیده
منابع مشابه
Integrative Self-Organizing Map—A Mean Pattern Model
We propose an integrative self-organizing map (iSOM) for exploring differential expression patterns across multiple microarray experiments. The algorithm is based on the assumption that observed differential expressions are random samples of a mean pattern model which is unknown a priori. The learning mechanism of iSOM is similar to the conventional SOM. The mean pattern model which underlies t...
متن کاملSteel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps
Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...
متن کاملConcurrent Self-Organizing Maps for Pattern Classification
We present a new neural classification model called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks. Each SOM of the system is trained individually to provide best results for one class only. We have considered two significant applications: face recognition and multispectral satellite image classification. For first application, we have u...
متن کاملPattern Analysis with Layered Self-Organizing Maps
1. Abstract Abstract— This paper defines a new learning architecture, Layered Self-Organizing Maps (LSOMs), that uses the SOM and supervised-SOM learning algorithms. The architecture is validated with the MNIST database of hand-written digit images. LSOMs are similar to convolutional neural nets (covnets) in the way they sample data, but different in the way they represent features and learn. L...
متن کاملOn the Analysis of Pattern Sequences by Self-organizing Maps on the Analysis of Pattern Sequences by Self-organizing Maps
This thesis is organized in three parts. In the rst part, the Self-Organizing Map algorithm is introduced. The discussion focuses on the analysis of the Self-Organizing Map algorithm. It is shown that the nonlinear nature of the algorithm makes it di cult to analyze the algorithm except in some trivial cases. In the second part the Self-Organizing Map algorithm is applied to several patterns se...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Engineering
سال: 2013
ISSN: 1947-3931,1947-394X
DOI: 10.4236/eng.2013.510b050