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Filters
Theory of Optimal Bayesian Feature Filtering
2019
Bayesian Analysis
Preserved Fulltext
First, optimal Bayesian feature selection under a general family of Bayesian models reduces to filtering if and only if the underlying Bayesian model assumes all features are mutually independent. ...
Optimal Bayesian feature filtering (OBF) is a supervised screening method designed for biomarker discovery. In this article, we prove two major theoretical properties of OBF. ...
Theory of Optimal Bayesian Feature Filtering ...
doi:10.1214/19-ba1182
fatcat:oluxigt62ffr3muys5pgkcjsb4
Theory of Optimal Bayesian Feature Filtering
[article]
2019
arXiv
pre-print
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First, optimal Bayesian feature selection under a general family of Bayesian models reduces to filtering if and only if the underlying Bayesian model assumes all features are mutually independent. ...
Optimal Bayesian feature filtering (OBF) is a supervised screening method designed for biomarker discovery. In this article, we prove two major theoretical properties of OBF. ...
Theory of Optimal Bayesian Feature Filtering: Supplementary Material A Ali Foroughi pour † and Lori A. Dalton † ...
arXiv:1909.03637v1
fatcat:vq4msu2s3nd2rhekpjgnzibije
A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition
2006
Pattern Recognition Letters
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At each level, we choose an optimal set of 2-D Gabor filters in the sense that Fisher's linear discriminant (FLD) measure is maximized and these Gabor filters are used to extract HGFs. ...
We present a method of handwritten numeral recognition, where we introduce hierarchical Gabor features (HGFs) and construct a Bayesian network classifier that encodes the dependence between HGFs. ...
Therefore, the selection of optimal Gabor filters with an efficient frequency is required to extract the relevant features for recognition. ...
doi:10.1016/j.patrec.2005.07.003
fatcat:tomsjg2ibzeyxnfd4h4563lynq
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
[article]
2020
arXiv
pre-print
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We therefore treat feature selection as a multi-objective optimization task. ...
Both methods make use of parameterized filter ensembles. ...
Bayesian optimization is used to find the optimal filter (among a set of possible filters) and the best feature selection rate. ...
arXiv:1912.12912v2
fatcat:sb32invxt5hzblxis4fegrm7kq
Bayesian Spike-Triggered Covariance Analysis
2011
Neural Information Processing Systems
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more flexible model for feature space inference. ...
It also allows us to employ Bayesian methods for regularization, smoothing, sparsification, and model comparison, and provides Bayesian confidence intervals on model parameters. ...
methods for smoothing and feature selection (estimation of the number of filters). ...
dblp:conf/nips/ParkP11
fatcat:23l64qn33bag3bjgsodmcyxcnq
Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization
2021
Computational Intelligence and Neuroscience
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Finally, the Bayesian Optimization algorithm (BOA) is used to optimize the LightGBM model to obtain the optimal hyperparameters. ...
In this paper, fetal movement can be efficiently recognized by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light ...
tuned LightGBM model using Bayesian Optimization. ...
doi:10.1155/2021/6252362
pmid:34925493
pmcid:PMC8677371
fatcat:d4plscr6uze5nm5tiecklqxmpa
Front Matter
[chapter]
2020
Optimal Bayesian Classification
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This book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions ...
The underlying random process might be a random signal/image for filtering, a Markov process for control, or a feature-label distribution for classification. ...
doi:10.1117/3.2540669.fm
fatcat:y5ipf7r2zre7db75varm2t27ai
Hierarchical Bayesian Network for Handwritten Digit Recognition
[chapter]
2003
Lecture Notes in Computer Science
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This paper introduces a hierarchical Gabor features(HGFs) and hierarchical bayesian network(HBN) for handwritten digit recognition. ...
The HGFs are extracted by the Gabor filters selected using a discriminant measure. ...
Next, the optimal Gabor filters are selected from the Gabor filter banks using a discriminant measure, and the HGFs are then extracted from the optimal Gabor filters. ...
doi:10.1007/3-540-44989-2_35
fatcat:c2ubqs4cu5f7hpjgjor6zym6di
Hierarchical Bayesian Network for Handwritten Digit Recognition
[chapter]
2003
Lecture Notes in Computer Science
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This paper introduces a hierarchical Gabor features(HGFs) and hierarchical bayesian network(HBN) for handwritten digit recognition. ...
The HGFs are extracted by the Gabor filters selected using a discriminant measure. ...
Next, the optimal Gabor filters are selected from the Gabor filter banks using a discriminant measure, and the HGFs are then extracted from the optimal Gabor filters. ...
doi:10.1007/3-540-36592-3_38
fatcat:6spox4zjprhztneaj5j4rimm4u
A Hybrid Ensemble Word Embedding based Classification Model for Multi-document Summarization Process on Large Multi-domain Document Sets
2021
International Journal of Advanced Computer Science and Applications
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Most of the conventional single document feature extraction measures are independent of contextual relationships among the different contextual feature sets for the document categorization process. ...
Contextual text feature extraction and classification play a vital role in the multi-document summarization process. ...
model to the conventional approaches for filtering optimal key features count for document clustering process on DUC 2002 dataset. ...
doi:10.14569/ijacsa.2021.0120918
fatcat:vsnwimayzfehld7rn4p43lty2u
FAST-LIO, Then Bayesian ICP, Then GTSFM
[article]
2022
arXiv
pre-print
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The first system is FL2BIPS which utilizes the iEKF algorithm FAST-LIO2 and Bayesian ICP PoseSLAM, whereas the second system is GTSFM, a structure from motion pipeline with factor graph backend optimization ...
NO: Pose SLAM is based solely on Bayesian ICP and no 3D features are optimized for. • Is loop closing used? ...
YES. • Filter or optimization-based: optimization-based, but jump-started by FAST-LIO2, which is filter-based. • Is the method causal? ...
arXiv:2210.00146v2
fatcat:hr6it47sunhdfnv3ar4jh7o6hi
Contactless Palm vein Authentication Using Deep Learning with Bayesian Optimization
2020
IEEE Access
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The training process is performed at every objective function evaluation, each with a different network structure and training options using a Bayesian optimization algorithm to find the optimal network ...
In this paper, we propose a palm vein authentication model using convolutional neural networks (CNN), which is the most popular deep learning architecture and Bayesian optimization. ...
Section 4 introduces the deep learning-based automated feature extraction method. Section 5 presents an overview of Bayesian optimization. ...
doi:10.1109/access.2020.3045424
fatcat:lng5xvugrrb7hnsxv7cgka52r4
Spam filtering by using Genetic based Feature Selection
2014
International Journal of Computer Applications Technology and Research
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Bayesian network and KNN classifiers have been taken into account in classification phase and spam base dataset is used. ...
Most algorithms try to present a data model depending on certain detection of small set of features. Unrelated features in the process of making model result in weak estimation and more computations. ...
[8] proposed Bayesian junk E-mail filter using bag-of-words representation and Naïve Bayes algorithm. Clark, et. al. ...
doi:10.7753/ijcatr0312.1018
fatcat:raslqttxrjhftgxvdwbovmswdu
Proposing an Optimization Algorithm for Employee Competencies Evaluation using Artificial Intelligence Methods: Bayesian Network and Decision Tree
2013
International Journal of Computer Applications
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features of the employee performance. ...
This paper keeps special focus on the employment of belief network including influence and Bayesian nets models in modeling the uncertainties and decision making process. ...
Decision Tree filtering features based on relative importance The above mentioned figure 4 shows the results of the optimization process. ...
doi:10.5120/12354-8666
fatcat:ctoe34mzsna6zj6t2e5uqxiswi
Bayesian optimization-based CNN framework for automated detection of brain tumors
2023
Balkan Journal of Electrical and Computer Engineering
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With Bayesian optimization and Gaussian process, we identified models with optimum architecture from hyperparameter combinations. We performed the training process with two different datasets. ...
These parameters, encompassing filter weights, fundamentally shape the performance of the model. ...
An intriguing observation we made using Bayesian optimization in both datasets is the decline in filter counts in the last layers. ...
doi:10.17694/bajece.1346818
fatcat:za3bw5crnjgydf66v6xii6qwci
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