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Prediction Algorithms: A Study
2018
Asian Journal of Computer Science and Technology
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Prediction algorithms make a prognosis of the future in a scientific way by analysing the data. They are being applied successfully to the problems in various fields and find good solutions. ...
It outlines the different types of prediction algorithms and the relevant publications on it. ...
It transforms the input space into a new space (F feature space) using a nonlinear mapping. Fuzzy support vector machine is accepted as a significant addition in the SVM family. ...
doi:10.51983/ajcst-2018.7.3.1896
fatcat:bteiqgxw2beabb4oe6lmasuyv4
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
2018
Future Internet
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In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark ...
DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. ...
Machine learning based NLEs, on the contrary, present a complexity that does not depend on the link parameters but on some signal parameters, for instance the number of constellation points and the number ...
doi:10.3390/fi11010002
fatcat:sy5ror2tvfdrfensdxcicgmirq
Graph clustering with Boltzmann machines
[article]
2022
arXiv
pre-print
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In doing so, we obtain a heuristic approximation to the intra-cluster density maximization problem. We use two variations of a Boltzmann machine heuristic to obtain numerical solutions. ...
Finally, we also note that both our clustering formulations, the distance minimization and K-medoids, yield clusters of superior quality to those obtained with the Louvain algorithm. ...
Acknowledgements The authors would like to thank Fujitsu Limited and Fujitsu Consulting (Canada) Inc. for providing financial support. ...
arXiv:2203.02471v2
fatcat:eyhokd354fexvhzbxbm6s4vdei
Resolving Wireless Sensor Networks Issues using Machine Learning Techniques: A Review
2021
International Journal for Research in Applied Science and Engineering Technology
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In order to provide a quick response for dynamic changes, Machine learning (ML) techniques can be applied on WSN. ...
In this paper, Machine learning techniques for solving various issues in WSN are presented; we discussed machine learning techniques for anomaly, fault, and event detection. ...
Based on the sample similarity the dataset is divided into k subsets as in SC and again based on the similarity features the distance between data points in testing and training set is measured. ...
doi:10.22214/ijraset.2021.37085
fatcat:tb4uujt5pbgftintfjbkezncfy
A MODIFIED KOHONEN SELF-ORGANIZING MAP (KSOM) CLUSTERING FOR FOUR CATEGORICAL DATA
2016
Jurnal Teknologi
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Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. ...
Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. ...
Acknowledgement The authors would like to thank Malaysia-Japan International Institute of Technology (MJIIT) for funding this research project thru a research grant with vote number 10H9. ...
doi:10.11113/jt.v78.9275
fatcat:gyf3forq5vgw3oqhpbv5x64xty
Evolutionary Machine Learning: A Survey
2022
ACM Computing Surveys
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), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). ...
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. ...
Evolutionary Support Vector Machine. The idea of support vector machines is based on an optimally separating hyper-plane. ...
doi:10.1145/3467477
fatcat:o6m3nekqfnaudjnxxoeferhine
A Bayesian Framework for Integrated Deep Metric Learning and Tracking of Vulnerable Road Users Using Automotive Radars
2021
IEEE Access
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In this work, we demonstrate the performance of the proposed Bayesian framework using several vulnerable user targets based on a 77 GHz automotive radar. ...
The tracker's performance is optimized due to a better separability of the targets. ...
However, one could also use a linear classifier such as the support vector machine (SVM). ...
doi:10.1109/access.2021.3077690
fatcat:6hbklgq6s5fn5me6utfu7sh4yi
FAST MEAN-SHIFT BASED CLASSIFICATION OF VERY HIGH RESOLUTION IMAGES: APPLICATION TO FOREST COVER MAPPING
2012
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Indeed, it is based on an optimization procedure to determine the modes of the pixels density. ...
This algorithm is up to 5 times faster than other fast MS algorithms while inducing a low loss in quality compared to the original MS version. ...
The authors acknowledge the Department of Sustainability and Environment (DSE) of Victoria (Australia) for the high resolution nearinfrared aerial photography. ...
doi:10.5194/isprsannals-i-7-111-2012
fatcat:d33gmste4beg5hx77c56yaqsli
FDive: Learning Relevance Models using Pattern-based Similarity Measures
[article]
2019
arXiv
pre-print
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2020 accepted arXiv:1907.12489v3 fatcat:dfpvnphtr5fw5ew356dhhlpgxyOther Versions
Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. ...
Therefore, analysts require automated support for the extraction of relevant patterns. ...
Classic machine learning techniques depend on a predefined set of features and a given distance function, chosen or even designed by experts based on their experience. ...
arXiv:1907.12489v2
fatcat:nkohfiqzpfhxtcdfmygntg7scq
On Feature Selection for Genomic Signal Processing and Data Mining
2007
Machine Learning for Signal Processing
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The curse of dimensionality has traditionally been a serious concern in many genomic applications. For example, the feature dimension of gene expression data is often in the order of thousands. ...
An effective data mining system lies in the representation of pattern vectors. ...
Acknowledgments The author wish to acknowledge the technical contributions of Dr. M. W. ...
doi:10.1109/mlsp.2007.4414275
fatcat:at3rnrj7u5eyrkohbnuqr6jrny
A Survey on Compiler Autotuning using Machine Learning
2018
ACM Computing Surveys
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Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. ...
This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and ...
The authors introduced a clustering algorithm to cluster sequences based on the similarity matrix by calculating the Euclidean distance between the two sequence vectors. ...
doi:10.1145/3197978
fatcat:2vgxveo2jfek5gd4hhtbqr22d4
Data Mining Techniques in High Content Screening: A Survey
2009
Journal of computer science and systems biology
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The analysis of the large amount of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. ...
Advanced microscopy and corresponding image analysis have evolved in recent years as a compelling tool for studying molecular and morphological events in cells and tissues. ...
The goal for support vector machines is to find a plane in this high-dimensional space that perfectly splits two or more sets of screening run. ...
doi:10.4172/jcsb.1000035
fatcat:5cez3vy64bfpvmkje23jmfualy
Integrating Dimension Reduction and Out-of-Sample Extension in Automated Classification of Ex Vivo Human Patellar Cartilage on Phase Contrast X-Ray Computed Tomography
2015
PLoS ONE
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The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the ...
The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. ...
Emmanuel Brun for his assistance with the data sharing process, Benjamin Mintz for his assistance in developing the annotation tool used in this study, Dr. ...
doi:10.1371/journal.pone.0117157
pmid:25710875
pmcid:PMC4339581
fatcat:4bilc3yv6jdyblxsppm5qw4tca
A Kernel Approach for Semisupervised Metric Learning
2007
IEEE Transactions on Neural Networks
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In this paper, we propose a kernel approach for semi-supervised metric learning and present in detail two special cases of this kernel approach. ...
In particular, some methods have been proposed for semi-supervised metric learning based on pairwise similarity or dissimilarity information. ...
As for the Gaussian window parameter σ used in the regularization term (Equation (16) ), we make it depend on the average squared Euclidean distance between all point pairs in the feature space: σ 2 = ...
doi:10.1109/tnn.2006.883723
pmid:17278468
fatcat:qfhret45nzf7hpk2hrpfxc67aa
Web Log Data Analysis by Enhanced Fuzzy C Means Clustering
2014
International Journal on Computational Science & Applications
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In this paper a novel clustering method to partition user sessions into accurate clusters is discussed. ...
The accuracy and various performance measures of the proposed algorithm shows that the proposed method is a better method for web log mining. ...
Classification by Support Vector Machines Support Vector Machines based on Structural Risk Minimization acts as one of the best approach for classification. ...
doi:10.5121/ijcsa.2014.4209
fatcat:vjmp3kpzyrhv3ng7qbvjjvbdma
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