Report / Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science. A

contributor

• Teknillinen korkeakoulu. Informaatiotekniikan laboratorio
• Teknillinen korkeakoulu. Neuroverkkojen tutkimusyksikkö

hasPart

• 3043 works that have been based on the Self-Organizing Map (SOM) method developed by Kohonen
• A class of neural networks for independent component analysis
• A family of fixed-point algorithms for independent component analysis
• A fast fixed-point algorithm for independent component analysis
• A fast implementation of the maximum-search function in adaptive solutions' CNAPS neurocomputer
• A modification of Kohonen's self-organizing algorithm : numerical studies
• Adaptive frequency estimation by MUSIC method
• Adaptive, associative, and self-organizing functions in neural computing
• An adaptive approach to segmentation of surface defects
• An algorithm for binary blind source separation
• An analysis of an associative text recognition method
• An operational cloud classifier based on a self-organized feature map
• Asymptotic level density for a class of vector quantization processes
• Automatic coloring of data according to its cluster structure
• Boosting the computing power in pattern recognition by unconventional architectures
• Comparison between classical and neu[t]ral network methods in texture recognition
• Computation of VQ and SOM point densities using the calculus of variations
• Computationally efficient approximation of a probabilistic model for document representation in the WEBSOM full-text analysis method
• Content-addressable memory
• Data averaging inside categories with the self-organizing map
• Development of a cloud classifier
• Fast evolutionary learning with batch-type self-organizing maps
• Independent component analysis by general non-linear Hebbian-like rules
• Independent component analysis by minimization of mutual information
• LVQ_PAK : the learning vector quantization program package
• MATLAB-ohjelmiston laajennus tehospektrin estimointiin
• Multidimensional image analysis : special course in information science III, autumn 1989
• Natural and artificial parallel computation
• Neural networks and adaptive pattern recognition
• Neural networks for speech recognition
• New approximations of differential entropy for independent component analysis and projection pursuit
• Newsgroup exploration with WEBSOM method and browsing interface
• Nonlinear PCA : algorithms and applications
• On the fourier methods for estimating the signal subspace
• Optimal code generation based on modular system descriptions
• Representation and separation of signals using nonlinear PCA type learning
• Self-organizing maps in error tolerant transmission of vector quantized images
• Self-organizing maps of symbol strings
• Self-organizing semantic maps
• Signaalinkäsittelyn ja neuraalilaskennan piirit ja arkkitehtuurit
• Signal processor code generation : an analytic study and practical consideration
• Signal processor implementation of some digital filter structures
• Simple neuron models for independent component analysis
• Sinusoidal frequency estimation by signal subspace approximation
• SOM_PAK : the self-organizing map program package
• Sparse code shrinkage : denoising of nongaussian data by maximum likelihood estimation
• Structures of welfare and poverty in the world discovered by the self-organizing map
• The nonlinear PCA learning rule and signal separation : mathematical analysis
• The speedy SOM
• Topology preservation in self-organizing maps
• Training mixture density HMMs with SOM and LVQ
• Use of temporal information in English vowel classification : experiments with the TIMIT database

inLanguage

• en
• mul

isPartOf

• Fennica

issn

• 0783-7445
• 0783-7453
• 1455-0784

name

• Raportti : A / Teknillinen korkeakoulu. Tietotekniikan osasto. Informaatiotekniikan laboratorio
• Raportti : B / Teknillinen korkeakoulu, tietotekniikan osasto, informaatiotekniikan laboratorio
• Rep. - Hels. Univ. Technol., Dep. Comput. Sci. Eng., Lab. Comput. Inf. Sci., A
• Rep. - Hels. Univ. Technol., Fac. Inf. Technol., Lab. Comp. Inf. Sci., A
• Rep. - Hels. Univ. Technol., Lab. Comput. Inf. Sci., B
• Report
• Report / Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science. A
• Report / Helsinki University of Technology, Faculty of Information Technology, Laboratory of Computer and Information Science. A
• Report / Helsinki University of Technology, Laboratory of Computer and Information Science. B
• Report / Helsinki University of Technology. Faculty of Information Technology. Laboratory of Computer and Information Scence. A
• Report / Helsinki University of Technology. Faculty of Information Technology. Laboratory of Computer and Information Science. A
• Report : B / Helsinki University of Technology, Faculty of Information Technology, Laboratory of Computer and Information Science
• Report : B / Helsinki University of Technology, Neural Networks Research Centre and Laboratory of Computer and Information Science
• Teknillinen korkeakoulu. Tietotekniikan osasto. Informaatiotekniikan laboratorio. Report. A
• Teknillinen korkeakoulu. Tietotekniikan osasto. Informaatiotekniikan laboratorio. Report. B

P60049

• <http://rdaregistry.info/termList/RDAContentType/1020>

sameAs

• <https://issn.org/resource/issn/0783-7445>
• <https://issn.org/resource/issn/0783-7453>
• <https://issn.org/resource/issn/1455-0784>

Instances

• 1987-1996 : Teknillinen korkeakoulu
• 1988/2001 : Teknillinen korkeakoulu
• 1996-1998 : Teknillinen korkeakoulu