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Volume Regularization for Binary Classification
2012
Neural Information Processing Systems
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We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. ...
Our learning algorithm seeks for a box of large volume that contains "simple" weight vectors which most of are accurate on the training set. ...
This is a property of many popular loss functions for binary classification, including the hinge-loss and its square used by SVMs [3, 22] , exp-loss used by boosting [9] , logistic-regression [11] ...
dblp:conf/nips/CrammerW12
fatcat:jvyn45dnbbg3ldajss3d2fczxy
Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Various attempts to improve the performance of binary classification model for bank tele-marketing data. ...
Therefore, this research proposes a data pre-processing algorithm for bank tele-marketing binary classification neural network. ...
This algorithm is then employed in order to develop the binary classification model for bank tele-marketing. ...
doi:10.35940/ijitee.c8472.019320
fatcat:wtgogc4yjnal7njr62spwppxmy
Differential Geometric Regularization for Supervised Learning of Classifiers
2016
International Conference on Machine Learning
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In experiments, we apply our regularization technique to standard loss functions for classification, our RBF-based implementation compares favorably to widely used regularization methods for both binary ...
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. ...
Acknowledgments We would like to thank Shivani Agarwal for her very helpful feedback on an earlier version of this paper. ...
dblp:conf/icml/BaiRWS16
fatcat:cwddymppfjfv7lcvzr7qnk4ytq
Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data
[article]
2017
arXiv
pre-print
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Most existing studies have been focusing on binary classification problems, e.g., distinguishing AD patients from normal control (NC) elderly or mild cognitive impairment (MCI) individuals from NC elderly ...
degeneration caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-category classification problem. ...
Instead of formulating the AD diagnosis as binary classification that accounts for 2 out 4 stages of AD progression, or as regular multi-category classification ignoring the progressive property of adjacent ...
arXiv:1709.01599v2
fatcat:b2td3jcxrnar5i7eou2jh6y4om
High-Order Graph Matching Based Feature Selection for Alzheimer's Disease Identification
[chapter]
2013
Lecture Notes in Computer Science
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Finally, the selected regional gray matter volume and cortical thickness features are fused in kernel space for classification. ...
In particular, we devise new regularization terms in sparse representation to impose high-order graph matching between the target vectors and the predicted ones. ...
To accomplish this, we devise the binary regularization term (B) as shown in Eq. (2) , and the ternary regularization term (T) as shown in Eq. (3) . ...
doi:10.1007/978-3-642-40763-5_39
fatcat:3rzxhlzffbalbharzrivjwwi7a
Class Probability Estimation via Differential Geometric Regularization
[article]
2016
arXiv
pre-print
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In experiments, we apply our regularization technique to standard loss functions for classification, our RBF-based implementation compares favorably to widely used regularization methods for both binary ...
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. ...
regularization approaches) are originally designed for binary classification, and rely on "one versus one", "one versus all" or more efficiently a binary coding strategy (Varshney & Willsky, 2010) to ...
arXiv:1503.01436v7
fatcat:fr2l2zo5rvaa7gfjbfo4uuypnm
Design and Development of RNN Anomaly Detection Model for IoT Networks
2022
IEEE Access
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Finally, a lightweight deep learning model for binary classification was proposed using LSTM, BiLSTM, and GRU based approaches. ...
Compared to current deep learning implementations, the proposed multiclass and binary classification model achieved high accuracy, precision, recall, and F1 score. ...
Layer normalization and activity regularization layers were used to develop novel RNN and CNNRNN based models for multiclass and binary classification. ...
doi:10.1109/access.2022.3176317
fatcat:3haetnnq2nfjxgpphsc7rvxgti
Bi3D: Stereo Depth Estimation via Binary Classifications
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Cost volumes are amenable to filtering and regularization due to their discrete nature, which makes them a powerful tool for stereo matching. ...
Our key contribution is the framing of depth estimation as a collection of binary classification tasks. ...
doi:10.1109/cvpr42600.2020.00167
dblp:conf/cvpr/BadkiTKKSG20
fatcat:5loa74n5tbdrrhi4q6aed2pkia
Water Detection through Spatio-Temporal Invariant Descriptors
[article]
2015
arXiv
pre-print
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The descriptors are used to locally classify the presence of water and a binary water detection mask is generated through spatio-temporal Markov Random Field regularization of the local classifications ...
Water detection is beneficial for appllications such as video search, outdoor surveillance, and systems such as unmanned ground vehicles and unmanned aerial vehicles. ...
Next to fusing temporal and spatial information for local water classification, Table 1 also indicates the effectiveness of regularization. ...
arXiv:1511.00472v2
fatcat:fcukvxh57jfipdmorxfrrf7ati
Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
2014
Frontiers in Aging Neuroscience
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MCI, (94.8 ± 7.3)% for AD vs. HC, (76.5 ± 11.1)% for MCI vs. HC, and (71.0 8.4)% for AD vs. MCI vs. HC, respectively. ± ...
modality to other modalities does help improve the classification accuracy; (3) HGM-FS works best among the three feature selection methods; (4) Some of the selected features are shared by all the feature ...
As we did for the binary classification, we perform similar analysis for the multiclass classification as well. ...
doi:10.3389/fnagi.2014.00260
pmid:25368574
pmcid:PMC4201101
fatcat:r434mxsvzbhnplxlri3gerbu3y
A Modified Maximal Divergence Sequential Auto-Encoder And Time Delay Neural Network Models For Vulnerable Binary Codes Detection
2020
IEEE Access
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THE MODIFIED MAXIMUM DIVERGENCE SEQUENTIAL AUTO-ENCODER (MDSAE) FOR BINARY VULNERABILITY DETECTION During classification, a classifier may get over-fitted due to its high complexity. ...
2016
VOLUME 4, 2016 ...
doi:10.1109/access.2020.2965726
fatcat:6tq6ktwpkjgd7ej2hqtdbneova
Binary Gaussian Process classification of quality in the production of aluminum alloys foams with regular open cells
2021
Procedia CIRP
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A data analysis methodology of experimental data, which is based on Binary Gaussian Process Classification, is presented. ...
A data analysis methodology of experimental data, which is based on Binary Gaussian Process Classification, is presented. ...
A latent function, distributed as a GP [6] , is considered as the basis for binary GP classification. ...
doi:10.1016/j.procir.2021.03.046
fatcat:piotaxhabveeznnykpmjkkpreu
Fast CSG voxelization by frame buffer pixel mapping
2000
Proceedings of the 2000 IEEE symposium on Volume visualization - VVS '00
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The result can be used for volume rendering and other applications. ...
Using 3D texture mapping and frame buffer pixel operations, the algorithm can interactively generate a binary volume of the CSG model. ...
These classification indices can then be mapped, by the PCM, to form a binary volume of the CSG model. ...
doi:10.1145/353888.353896
dblp:conf/vvs/FangL00
fatcat:pspigbujojeobhvyxy56bejxyq
Fast CSG Voxelization by Frame Buffer Pixel Mapping
2000
2000 IEEE Symposium on Volume Visualization (VV 2000)
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The result can be used for volume rendering and other applications. ...
Using 3D texture mapping and frame buffer pixel operations, the algorithm can interactively generate a binary volume of the CSG model. ...
These classification indices can then be mapped, by the PCM, to form a binary volume of the CSG model. ...
doi:10.1109/vv.2000.10005
fatcat:6atr2somwjcgrev36u6nri7ria
Breast Cancer Classification from Histopathological Images Using Resolution Adaptive Network
2022
IEEE Access
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For the BACH 2018 dataset, binary and multiclass classifications on patch and image levels are performed. ...
Experimental results show that the best accuracies for multiclass and binary classifications at the image level are 97.75% and 99.25%, respectively. ...
Spanhol for providing the BreaKHis dataset and Araújo Teresa for providing the BACH 2018 dataset. ...
doi:10.1109/access.2022.3163822
fatcat:rek6f4ersfborgs22bqnqaw4ki
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