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Evolving
connectionist
systems for adaptive decision support with application in ecological data modelling : : a thesis submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD), 2009 /
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Soltic, Snjezana
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Comparative analysis of traditional machine learning methods and spiking neural networks for spatio-temporal data mining: a thesis submitted to Auckland University of Technology in...
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Nayak, Parag Ganesh
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Novel integrated methods of evolving spiking neural network and particle swarm optimisation : a thesis submitted to Auckland University of Technology in fulfilment of the requireme...
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Hamed, Haza Nuzly Bin Abdull
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Heterogeneous probabilistic models for optimisation and modelling of evolving spiking neural networks : a thesis submitted to Auckland University of Technology in fulfillment [sic]...
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Schliebs, Stefan
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Integrated multi-model framework for adaptive multiple time-series analysis and modelling : a thesis submitted to Auckland University of Technology in fulfilment of the requirement...
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Widiputra, Harya
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Soft systems analysis of ecosystems: thesis submitted to Auckland University of Technology in fulfillment of the degree of Doctor of Philosophy, 2004 /
by
Shanmuganathan, Subana
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Items from Articles & more search
An Innovative Method to analyze the Accuracy and Prediction rate of Handwritten Digit Recognition with Dimensionality Reduction Algorithm by comparing with Connectionist Temporal C...
by
Prathyusha, Pranathi Sai
, et al.
Training a Singing Transcription Model Using Connectionist Temporal Classification Loss and Cross-Entropy Loss
by
Wang, J.
, et al.
Assessment of quality of health services in fractal-organized connectionist networks
by
Zinca, Gabriela
, et al.
Evolving Connectionist System and Hidden Semi-Markov Model for Learning-Based Tool Wear Monitoring and Remaining Useful Life Prediction
by
Lin, M.
, et al.
Query-by-Example on-Device Keyword Spotting using Convolutional Recurrent Neural Network and Connectionist Temporal Classification
by
Meirong, Li
, et al.
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