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Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)
[article]
2019
arXiv
pre-print
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In this paper, a multi-layer architecture for OCC is proposed by stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion. ...
The last layer of this proposed architecture is Graph-Embedded regression-based one-class classifier. ...
One is referred as Local variance based Graph-Embedded Multi-layer KRR for One-class Classification (LM KOC), and other is referred as Global variance based Graph-Embedded Multi-layer KRR for One-class ...
arXiv:1904.06491v1
fatcat:k3j6jfyjrzeozi4nlv4wm6scju
Road Roughness Estimation Using Machine Learning
[article]
2021
arXiv
pre-print
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We compared well-known supervised machine learning models such as linear regression, naive Bayes, k-nearest neighbor, random forest, support vector machine, and the multi-layer perceptron neural network ...
The models are trained on an optimally selected set of features computed in the temporal and statistical domain. ...
ACKNOWLEDGMENTS The authors would like to thank the Innovation Fund Denmark [54] , who supported this work with the grant "Live Road Assessment tool based on modern car sensors LiRA" . ...
arXiv:2107.01199v1
fatcat:hgmndicepndgleqapbb3wbc4pu
Adversarial Representation Learning With Closed-Form Solvers
[article]
2021
arXiv
pre-print
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We model them as kernel ridge regressors and analytically determine an upper-bound on the optimal dimensionality of representation. ...
Performance wise, when the target and sensitive attributes are dependent, OptNet-ARL learns representations that offer a better trade-off front between (a) utility and bias for fair classification and ...
Treating the attributes as vectors enables us to consider both multi-class classification and regression under the same formulation. ...
arXiv:2109.05535v1
fatcat:ls4meqbl7ba25ju744fuoqeofu
Face and ECG Based Multi-Modal Biometric Authentication
[chapter]
2011
Advanced Biometric Technologies
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Classification of facial images Support Vector Machine is a supervised learning algorithm used for classification in two classes. ...
The goal in GDA is maximization of between-class variance and minimization of within-class variance. ...
The methods for human identity authentication based on biometrics â€" the physiological and behavioural characteristics of a person have been evolving continuously and seen significant improvement in performance ...
doi:10.5772/21842
fatcat:t36hkv7j6ndtvatte4jptieod4
Complex and Hypercomplex-Valued Support Vector Machines: A Survey
2019
Applied Sciences
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Thus, several interesting applications can be developed using these types of data and algorithms, such as signal processing, pattern recognition, classification of electromagnetic signals, light, sonic ...
The output layer provides a probability for each class based on Bayes' theorem. This is an example of the application of a complex SVM with real-valued data. ...
Nonlinear channel equalization (NCE) applications using the kernel ridge regression (KRR) with quaternion kernels are shown in [57] . ...
doi:10.3390/app9153090
fatcat:rarahjfqgjacfo3dksm7ya2vfy
A Deep Learning Model to Predict Congressional Roll Call Votes from Legislative Texts
2020
Zenodo
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The PTCN's custom architecture provides elements enabling adaptation to the input's variance from adjustment to the kernel weights over time. ...
The convolutional layers and the LSTM layers automatically recognize features from the input data's latent space. ...
L2 is Ridge Regression. The main difference between the two methods is the penalty term. ...
doi:10.5281/zenodo.4420869
fatcat:ka45plwukrcqbpmdubhheclab4
Evaluating Predictive Uncertainty Challenge
[chapter]
2006
Lecture Notes in Computer Science
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Participants had to compete on a number of regression and classification tasks, and were evaluated by both traditional losses that only take into account point predictions and losses we proposed that evaluate ...
and classification problems. ...
First, a probabilistic classifier based on Radford Neal's FBM software
Acknowledgements Many thanks to Olivier Chapelle for beta testing, to Yoshua Bengio for providing the Outaouais and Gatineau datasets ...
doi:10.1007/11736790_1
fatcat:qckw4n6ayzfvloihpo5mnura3a
Text Classification Algorithms: A Survey
2019
Information
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However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. ...
In thispaper, a brief overview of text classification algorithms is discussed. ...
Gerber for his feedback and comments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/info10040150
fatcat:qfmjtzsaoreahdwdwlfhymjtru
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
[article]
2022
arXiv
pre-print
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A concatenation of PPVs from all kernels is the input feature vector to a Ridge regression classifier. ...
Finally, the selected kernels in the best state vector are utilized to train the Ridge regression classifier with the selected kernels. ...
Genetic algorithm (GA) is one of the earliest methods for discovering a combination of connections for enhancing training of multi-layer perceptron models (MLPs) [44] . ...
arXiv:2203.03445v2
fatcat:3iegx56thbdzhexpxki5kawkxa
An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
2021
IEEE Access
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and Ridge Regression for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated ...
, Logistic Regression, RFE, and Ridge. ...
doi:10.1109/access.2021.3092228
fatcat:wtwvf6fn6ndhzfdiszfmj5taa4
Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?
[article]
2021
arXiv
pre-print
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We compared these five methods concerning their performance in QSAR scenarios using different classification and regression datasets. ...
To close this gap, we reviewed the literature on methods for molecular embeddings and reproduced three unsupervised and two supervised molecular embedding techniques recently proposed in the literature ...
Acknowledgments The authors thank Chris Whidden, Jason Newport and Lu Yang for their technical support with the DeepSense cluster. ...
arXiv:2104.02604v2
fatcat:3lrokwg7ujeu3gp6hdltusjoiu
Reconciling modern machine learning practice and the bias-variance trade-off
[article]
2019
arXiv
pre-print
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Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine learning practice. ...
We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. ...
We thank Nvidia for donating GPUs used for this research. ...
arXiv:1812.11118v2
fatcat:b2o723pgdzejlkctgcminq24oi
Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech
2021
Frontiers in Aging Neuroscience
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good predictive performance for detecting AD based on speech. ...
There has been a recent proliferation of classification models for AD, but these vary in the datasets used, model types and training and testing paradigms. ...
The number of features is optimized for all models. For ridge regression, the number of features is jointly optimized with the coefficient for L2 regularization, α. ...
doi:10.3389/fnagi.2021.635945
pmid:33986655
pmcid:PMC8110916
fatcat:zcn3wwf47bf4fnbwiaedk5l4ba
Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
2021
Scientific Reports
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For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). ...
For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). ...
The non-linear classifiers used were neural network, support vector machine with a polynomial kernel (svmPoly), SVM with a radial kernel (svmRad), and multi-layer perceptron (MLP). ...
doi:10.1038/s41598-021-90032-w
pmid:34006893
pmcid:PMC8131619
fatcat:6yjbacvuirbc7g4iydmcfdyciy
Classification of developmental and brain disorders via graph convolutional aggregation
[article]
2023
arXiv
pre-print
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While graph convolution based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification ...
We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI), for the prediction ...
It is important to note that for multi-class classification problems, the label of each node i (or its final output embedding z i ) in the labeled set D l can be represented as a C-dimensional one-hot ...
arXiv:2311.07370v2
fatcat:6evsjxbtjfeklaq4pfx3f74p3q
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