Publications in computer and information science / Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science. Report A

contributor

• Teknillinen korkeakoulu. Informaatiotekniikan laboratorio

hasPart

• A Bayesian approach to overlearning in ICA
• A fast semi-blind source separation algorithm
• A neural network that emphasizes or suppresses visual objects according to eye movements
• Adaptive and statistical approaches in conceptual modeling
• Advanced analysis methods for 3G cellular networks
• Associative clustering (AC) : technical details
• Associative clustering by maximizing a Bayes factor
• Clustering and visualization of large protein sequence databases by means of an extension of the self-organizing map
• Convergence of a stochastic semisupervised clustering algorithm
• Emergence of linguistic representations by independent component analysis
• Expectation maximization algorithms for conditional likelihoods
• Fast fixed-point algorithms for Bayesian blind source separation
• Finite sequential information bottleneck (fsIB)
• Inferring relevance from eye movements : feature extraction
• Metrics induced by maximizing mutual information
• Natural image patch statistics conditioned on activity of an independent component
• Neural networks for blind separation with unknown number of sources
• Regularized discriminative clustering
• Relevance feedback from eye movements for proactive information retrieval
• Second-order ICA in machine vibration analysis
• Self-organizing tactile maps
• Semisupervised clustering based on conditional distributions in an auxiliary space
• Simulating language learning in community of agents using self-organizing maps
• SOM toolbox for Matlab 5
• Text retrieval using self-organized document maps
• Unsupervised learning of nonlinear dynamic state-space models
• Unsupervised morpheme segmentation and morphology induction from text corpora using Morfessor 1.0
• Using visualization, variable selection and feature extraction to learn from industrial data
• Using visualization, variable selection and feature extraction to learn from industrial data

inLanguage

• en

isPartOf

• Fennica

issn

• 1456-2243

name

• Publ. comput. inf. sci., Rep. A
• Publications in computer and information science
• Publications in computer and information science / Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science. Report
• Publications in computer and information science / Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science. Report A
• Publications in computer and information science / Helsinki University of Technology. Report A
• Publications in computer and information science / Teknillinen korkeakoulu. A
• Publications in Computer and Information Science / Teknillinen korkeakoulu. A
• Publications in computer and information science / Teknillinen korkeakoulu. Report A
• Publications in computer and information science : Report A
• Publications in Computer and Information Science : Report E
• Publications in Computer and Information Science. A
• Publications in computer and information science. Report A
• Publications in Computer and Information Science. Report A

P60049

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

sameAs

• <https://issn.org/resource/issn/1456-2243>

Instances

• 1998-2006 : Teknillinen korkeakoulu
• 2006/2007 : Teknillinen korkeakoulu, e-book