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Multi-view Discriminative Sequential Learning [chapter]

Ulf Brefeld, Christoph Büscher, Tobias Scheffer
2005 Lecture Notes in Computer Science  
The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised hidden Markov perceptron, and a semi-supervised  ...  hidden Markov support vector learning algorithm.  ...  We derived the multi-view HM perceptron as well as multi-view 1-norm and 2norm HM SVMs.  ... 
doi:10.1007/11564096_11 fatcat:mmsndpu65rdcjklv3ztr575jki

Page 448 of Linguistics and Language Behavior Abstracts: LLBA Vol. 25, Issue 1 [page]

1991 Linguistics and Language Behavior Abstracts: LLBA  
; 9102058 automatic speech recognition, hidden Markov model improvement, new estimation techniques; 9102076 automatic speech recognition, multi-layered neural networks use; empirical data; 9102063 automatic  ...  role; 9102070 automatic speech recognition, Alpha computation, hidden Markov method discrimination; 9102060 automatic speech recognition, cepstral vs feature vectors, transitional vs instantaneous information  ... 

Bayesian Learning Neural Network Techniques to Forecast Mobile Phone User Location

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Conventional Multi Layer Perceptron can be viewed as particular arrangement from the position come back by the Bayesian 1.  ...  The Markov chain Monte Carlo techniques for Multi Layer Perceptron software package has been created in Matlab.  ... 
doi:10.35940/ijitee.i8023.078919 fatcat:ultea725unhw5bscotypwr6b7a

Isolated digit recognition experiments using the multi-layer perceptron

S.M. Peeling, R.K. Moore
1988 Speech Communication  
The results, for this particular task, show that the recognition accuracy obtained using the multi-layer perceptron is comparable with that from using hidden Markov modelling.  ...  A comparison is made with established techniques such as dynamic time-warping and hidden Markov modelling applied to the same data.  ...  Multi-Layer Networks The units in a multi-layer perceptron are configured in layers such that there is a layer of input units, any number of intermediate layers, and a layer of output units.  ... 
doi:10.1016/0167-6393(88)90057-x fatcat:7vbimioak5brlafapbxjcsrcpu

Bayesian Approach to Neuro-Rough Models [article]

Tshilidzi Marwala, Bodie Crossingham
2007 arXiv   pre-print
This paper proposes a neuro-rough model based on multi-layered perceptron and rough set. The neuro-rough model is then tested on modelling the risk of HIV from demographic data.  ...  Multi-layer Perceptron Model The other component of the neuro-rough model is the multi-layered network.  ...  This paper proposes a combined architecture that takes elements from both rough sets and multi-layered perceptron neural networks.  ... 
arXiv:0705.0761v3 fatcat:2mwp63bwmzahbc7tj5wx2nibwe

Application of Machine Learning for Dragline Failure Prediction

Amir Taghizadeh, Nuray Demirel, M. Tyulenev, S. Zhironkin, A. Khoreshok, S. Voth, M. Cehlar, Y. Tan
2017 E3S Web of Conferences  
The study methodology consists of three algorithms as: i) implementation of K-Nearest Neighbors, ii) implementation of Multi-Layer Perceptron, and iii) implementation of Radial Basis Function.  ...  Schematic view of multi-layer perceptron network In this study, MLP with four hidden layers and five processing neuron in each layer has been utilized.  ...  In this study, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) algorithms as methods of ANN, which are supervised training methods, have been utilized (i.e. ANN is an approach of ML).  ... 
doi:10.1051/e3sconf/20171503002 fatcat:s5w4fddst5bxvj4x4rpsaj3x3m

A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks [article]

Tariq M. Khan, Antonio Robles-Kelly
2020 arXiv   pre-print
In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons.  ...  We then make use of measurable operators to define the states of the network in a manner consistent with a Markov process.  ...  Note that UŶ can be viewed as a representation of a sequence of measurable states that define a Markov process with a transition probability matrix.  ... 
arXiv:2009.13264v1 fatcat:teliwerjfjdideqgnci65odyxm

Land-cover Classification and Mapping for Eastern Himalayan State Sikkim [article]

Ratika Pradhan, Mohan P. Pradhan, Ashish Bhusan, Ronak K. Pradhan, M. K. Ghose
2010 arXiv   pre-print
Gigandet et.al. have suggested region based satellite image classification that combines unsupervised segmentation with supervised segmentation using Gaussian hidden Markov random field and Support Vector  ...  RESULTS & CONCLUSION The LISS III image of the state Sikkim is classified using improvised k-Means, Unsupervised ANN using Multi-Layer Perceptron and Supervised ANN using Backpropagation Algorithm to train  ... 
arXiv:1003.4087v1 fatcat:jucvsooda5fa7jllr6tecpzbna

Design of a Neural Networks Classifier for Face Detection

F. Smach, M. Atri, J. Mitéran, M. Abid
2006 Journal of Computer Science  
The objective of this work is to implement a classifier based on neural networks MLP (Multi-layer Perceptron) for face detection. The MLP is used to classify face and non-face patterns.  ...  MULTI-LAYERS PERCEPTRON The MLP neural network [1] has feedforword architecture within input layer, a hidden layer, and an output layer.  ...  A Multi-Layers Perceptron (MLP) is a particular of artificial neural network [7] .  ... 
doi:10.3844/jcssp.2006.257.260 fatcat:xct7dqvw7nf3nnx7nuu4tye6aa

Design Of A Neural Networks Classifier For Face Detection

F. Smach, M. Atri, J. Mitéran, M. Abid
2007 Zenodo  
The objective of this work is to implement a classifier based on neural networks MLP (Multi-layer Perceptron) for face detection. The MLP is used to classify face and non-face patterns.  ...  MULTI-LAYERS PERCEPTRON The MLP neural network [1] has feedforword architecture within input layer, a hidden layer, and an output layer.  ...  A Multi-Layers Perceptron (MLP) is a particular of artificial neural network [7] .  ... 
doi:10.5281/zenodo.1061160 fatcat:pszjer5dl5bu3ehfyraalsz5sq

Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

Ruafan Liao, Woraphon Yamaka, Songsak Sriboonchitta
2020 IEEE Access  
INDEX TERMS Exchange rate volatility, neural networks, Markov-switching Beta-t-EGARCH.  ...  Given the exchange rate's nonlinear and time-varying characteristics, we introduce the neural networks (NN) approach to enhance the Markov Switching Beta-Exponential Generalized Autoregressive Conditional  ...  We note that three layers, namely one input layer, one hidden layer, and one output layer, are considered in the multi-layer perceptron neural network.  ... 
doi:10.1109/access.2020.3038564 fatcat:27ovlgomvjacbnlsef7csomnci

Recognition Techniques for Online Arabic Handwriting Recognition Systems

Mustafa Ali Abuzaraida, Akram M. Zeki, Ahmed M. Zeki
2012 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT)  
However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others.  ...  Later on, [9] a system based on Hidden Markov Models (HMMs) was developed to resolve most of the difficulties in recognizing Arabic script.  ...  The methods is Hidden Markov ommonly in the field of text ametric methods are directly The most common non-parametric methods are nearest neighbors.  ... 
doi:10.1109/acsat.2012.14 fatcat:y5btkfworzfohfbjxd5b64kqru

Email Category Prediction

Aston Zhang, Lluis Garcia-Pueyo, James B. Wendt, Marc Najork, Andrei Broder
2017 Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion  
We consider two types of neural networks: multilayer perceptrons (MLP), a type of feedforward neural network; and long short-term memory (LSTM), a type of recurrent neural network.  ...  We find that the prediction accuracy of neural networks vastly outperforms the Markov chain approach, and that LSTMs perform slightly better than MLPs.  ...  Section 4 reviews Markov chains, multi-layer perceptrons and long short-term memory. Section 5 describes our experimental evaluation.  ... 
doi:10.1145/3041021.3055166 dblp:conf/www/ZhangPWNB17 fatcat:x2ecev2quve3loknfcpm72vvni

A Survey On Semi-Supervised Learning Techniques
English

V. Jothi Prakash, Dr. L.M. Nithya
2014 International Journal of Computer Trends and Technology  
In [7] , semi-supervised learning methods by using two discriminative sequence learning algorithms -the Hidden Markov (HM) perceptron and Support Vector Machines (SVM) a multi-view HM perceptron as well  ...  The label sequence will be predicted by each view for every sample i if it is unlabeled or labeled analogous to the single-view hidden Markov perceptron.  ... 
doi:10.14445/22312803/ijctt-v8p105 fatcat:6ai7xan6cngpjk2g2adntninoa

Structure-perceptron learning of a hierarchical log-linear model

Long Zhu, Yuanhao Chen, Xingyao Ye, Alan Yuille
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
We demonstrate that the algorithm achieves the state of the art performance by evaluation on public dataset (horse and multi-view face).  ...  We introduce the structure-perceptron algorithm to estimate the parameters of the HLLM in a discriminative way.  ...  Experiment II: Multi-view Face Alignment The task of multi-view face alignment has been much more thoroughly studied than horse parsing.  ... 
doi:10.1109/cvpr.2008.4587344 dblp:conf/cvpr/ZhuCYY08 fatcat:rg7zeeqmpbgszphttqwmqytd64
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