Search Results - (( some applications learning algorithm ) OR ( its application window algorithm ))

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    SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA by AHMED IZZELDIN, HUZAIFA TAWFEIG

    Published 2012
    “…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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    Thesis
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    Early detection of dengue disease using extreme learning machine by Suhaeri, Suhaeri, Mohd Nawi, Nazri, Fathurahman, Muhamad

    Published 2018
    “…The back propagation neural network is one of the popular machine learning technique that capable of learning some complex relationship and had been used in many applications. …”
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    Article
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    Multi-Backpropagation network by Wan Ishak, Wan Hussain, Siraj, Fadzilah, Othman, Abu Talib

    Published 2002
    “…The learning mechanism for Neural Network is its learning algorithm. …”
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    Conference or Workshop Item
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    A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

    Published 2006
    “…An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. …”
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    Proceeding Paper
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    Three-term backpropagation algorithm for classification problem by Saman, Fadhlina Izzah

    Published 2006
    “…Standard Backpropagation Algorithm (BP) is a widely used algorithm in training Neural Network that is proven to be very successful in many diverse application. …”
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    Thesis
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    Applying learning to filter text by Sainin, Mohd Shamrie

    Published 2005
    “…Text filtering has been a successful application especially in e-mail filtering. The use of probabilistic approaches such as naïve Bayes algorithm is the effective algorithms currently known for learning to filter or classify text document.Naïve Bayes algorithm is one of the algorithms in Machine Learning that manipulates probability estimation or reasoning about the observed data.The growing of bulk e-mail or known as spam e-mail becomes a threat to users’ privacy and network load and in the case of e -mail filtering,naïve Bayes classifier can be trained to automatically detect spam messages.Similar to the e-mail, forum application may be misused by the user to send bad messages and in some extent may offence other readers.Forum filtering may be less important compared to e-mail spam filtering; however there is a possibility of using naïve Bayes to learn the messages and automatically detect bad messages.Most of the forum application found in the web is applying keyword based text filtering which scan the words and change the detected words into certain representation.Instead of defining a set of keywords to filter the forum messages, this paper will explains the experiment in applying a learning to filter text especially in the educational and anonymous forum message, where there is no user registration required to submit messages.…”
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    Conference or Workshop Item
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    The Estimation of Air Temperature from NOAA/AVHRR Images and the study of NDVI-Ts impact by Matori, A.N, Hassaballa, A. Haleem

    Published 2011
    “…From the brightness temperature in the images thermal bands 4&5 and using ε the surface temperature Ts was extracted using three different split-window algorithms. The application of the three formulas showed a homogenous results specially when using Becker et al (1990) and Uliveri et al. (1994) algorithms. …”
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    Article
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    Stock indicator scanner customization tool using deep reinforcement learning by Cheong, Desmond YongHong

    Published 2022
    “…This project will deliver a web application with dynamic stock prediction model based on deep reinforcement learning or more particularly, Deep Q-Network (DQN) algorithm which enable input customization. …”
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    Final Year Project / Dissertation / Thesis