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Prediction Algorithms: A Study

S. Santha Subbulaxmi, G. Arumugam
2018 Asian Journal of Computer Science and Technology  
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

Elias Giacoumidis, Yi Lin, Jinlong Wei, Ivan Aldaya, Athanasios Tsokanos, Liam Barry
2018 Future Internet  
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]

Pierre Miasnikof, Mohammad Bagherbeik, Ali Sheikholeslami
2022 arXiv   pre-print
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

Harshitha S
2021 International Journal for Research in Applied Science and Engineering Technology  
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


Azlin Ahmad, Rubiyah Yusof
2016 Jurnal Teknologi  
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

Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi
2022 ACM Computing Surveys  
), 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

Anand Dubey, Avik Santra, Jonas Fuchs, Maximilian Lubke, Robert Weigel, Fabian Lurz
2021 IEEE Access  
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


S. Boukir, S. Jones, K. Reinke
2012 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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]

Frederik L. Dennig, Tom Polk, Zudi Lin, Tobias Schreck, Hanspeter Pfister, Michael Behrisch
2019 arXiv   pre-print
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

S.Y. Kung
2007 Machine Learning for Signal Processing  
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

Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, Cristina Silvano
2018 ACM Computing Surveys  
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

Karol Kozak
2009 Journal of computer science and systems biology  
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

Mahesh B. Nagarajan, Paola Coan, Markus B. Huber, Paul C. Diemoz, Axel Wismüller, Qinghui Zhang
2015 PLoS ONE  
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

Dit-Yan Yeung, Hong Chang
2007 IEEE Transactions on Neural Networks  
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

V .Chitraa, Antony Selvadoss Thanamani
2014 International Journal on Computational Science & Applications  
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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