Search · four archives

Results for SVM decoding

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

24 papers · ranked by Valyu relevance

01
BbioRxiv13 Dec 2025
Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data
Guanghui Zhang, Xinran Wang, Steven J. Luck
Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the signal-to-noise ratio. However, the impact of…
81relevance
02
AarXiv21 Jun 2021
Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system
Hui‐Ping Yin, Hai‐Peng Ren
To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a…
78relevance
03
BbioRxiv3 Oct 2025
Feedforward and feedback population dynamics during binocular conflict in mouse visual cortex
Melina Timplalexi, William M. Connelly, Adam Ranson
Binocular rivalry arises when incongruent images are presented to the two eyes, producing stochastic alternations in perceptual dominance. While rivalry has been extensively studied in species with highly developed binocular vision, it is unclear whether similar representational dynamics occur in the mouse, a model…
78relevance
04
AarXiv4 Dec 2020
Joint Channel Estimation and Data Decoding using SVM-based Receivers.
Sami Akın, Maxim Penner, Jürgen Peissig
—Modern communication systems organize receivers in blocks in order to simplify their analysis and design. However, an approach that considers the receiver design from a wider perspective rather than treating it block-by-block may tak e advantage of the impacts of these blocks on each other and provide better…
77relevance
05
PPubMed2 Mar 2023
Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
Erick Lamilla, Christian Sacarelo, Manuel S. Alvarez-Alvarado, Arturo Pazmino + 4 more
'Arturo Pazmino' 'Peter Iza' 'Yichuang Sun' 'Haeyoung Lee' 'Oluyomi Simpson'] Based on orbital angular momentum (OAM) properties of Laguerre-Gaussian beams LG( $p,ℓ$), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an…
76relevance
06
PPubMed6 Feb 2018
Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate
Subash Padmanaban, Justin Baker, Bradley Greger
Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve…
75relevance
07
BbioRxiv1 Jun 2023
Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks
Yun Liang, Ke Bo, Sreenivasan Meyyappan, Mingzhou Ding
Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive…
74relevance
08
PPubMed6 Jan 2015
The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data
Martin N. Hebart, Kai Görgen, John-Dylan Haynes
The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional…
74relevance
09
PPubMed6 Mar 2018
Accurate Decoding of Short, Phase-Encoded SSVEPs
Ahmed Youssef Ali Amer, Benjamin Wittevrongel, Marc M. Van Hulle
Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain-computer interfacing (BCI) is assessed. The novel features are based on phase estimation and correlations between target…
74relevance
10
BbioRxiv11 Nov 2023
Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses
Carlos Daniel Carrasco, Brett Bahle, Aaron Matthew Simmons, Steven J. Luck
Multivariate pattern analysis approaches can be applied to the topographic distribution of event-related potential (ERP) signals to ‘decode’ subtly different stimulus classes, such as different faces and different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used…
74relevance
11
PPubMed20 Nov 2012
Nonlinear Demodulation and Channel Coding in EBPSK Scheme
Xianqing Chen, Lenan Wu
The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) performance. However, the conventional threshold decision cannot achieve the optimum performance, and the SIF brings more difficulty in…
73relevance
12
PPubMed28 Mar 2013
Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System
Xian-Qing Chen, Le-Nan Wu
The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is…
70relevance
13
BbioRxiv28 Mar 2026
A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics
G. Bilodeau, A. Miao, G. Gagnon-Turcotte, C. Ethier + 1 more
Bidirectional interfaces combined with neural de-coding algorithms are essential for closed-loop (CL) neuromodulation, enabling simultaneous neural monitoring and responsive optogenetic stimulation. However, implementing these capabilities in compact wireless headstages for freely moving animals remains challenging, as…
68relevance
14
BbioRxiv7 Nov 2019
Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs
Shoeb Shaikh, Rosa So, Tafadzwa Sibindi, Camilo Libedinsky + 1 more
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of…
67relevance
15
AarXiv2021
SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO Systems
Ly V. Nguyen, A. Lee Swindlehurst, Duy H. N. Nguyen
—The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of lowresolution ADCs causes significant distortions in the received signals and makes the channel…
66relevance
16
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
65relevance
17
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
64relevance
18
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
61relevance
19
AarXiv2013
Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction
Savinderjit Kaur, Veenu Mangat
— Data Mining is being actively applied to stock market since 1980s. It has been used to predict stock prices, stock indexes, for portfolio management, trend detection and for developing recommender systems. The various algorithms which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different hybrid…
59relevance
20
PPubMed27 Sep 2020
Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface
Peng Zhang, Lianying Chao, Yuting Chen, Xuan Ma + 4 more
'Jiping He' 'Jian Huang' 'Qiang Li'] Background: For the nonstationarity of neural recordings in intracortical brain-machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based…
57relevance
21
CchemRxiv5 Apr 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
56relevance
22
AarXiv2012
Handwritten digit Recognition using Support Vector Machine
Anshuman Sharma
Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system…
54relevance
23
AarXiv2014
Support Vector Machine Model for Currency Crisis Discrimination
Arindam Chaudhuri
Support Vector Machine (SVM) is powerful classification technique based on the idea of structural risk minimization. Use of kernel function enables curse of dimensionality to be addressed. However, proper kernel function for certain problem is dependent on specific dataset and as such there is no good method on choice…
53relevance
24
CchemRxiv20 Jul 2023
Machine Learning Classification can SignificantlyReduce the Cost of Calculating the HamiltonianMatrix in CI Calculations
Chen Qu, Paul Houston, Qi Yu, Riccardo Conte + 3 more
Hamiltonian matrices in electronic and nuclear contexts are highly compute-intensive to calculate, mainly due to the cost for the potential matrix. Typically these matrices contain many off-diagonal elements that are orders of magnitude smaller than diagonal elements. We illustrate that here for vibrational H-matrices…
50relevance

Results for SVM decoding

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data

Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

Feedforward and feedback population dynamics during binocular conflict in mouse visual cortex

Joint Channel Estimation and Data Decoding using SVM-based Receivers.

Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning

Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

Accurate Decoding of Short, Phase-Encoded SSVEPs

Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses

Nonlinear Demodulation and Channel Coding in EBPSK Scheme

Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System

A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics

Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO Systems

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Krein Support Vector Machine Classification of Antimicrobial Peptides

Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Handwritten digit Recognition using Support Vector Machine

Support Vector Machine Model for Currency Crisis Discrimination

Machine Learning Classification can SignificantlyReduce the Cost of Calculating the HamiltonianMatrix in CI Calculations

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data

Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

Feedforward and feedback population dynamics during binocular conflict in mouse visual cortex

Joint Channel Estimation and Data Decoding using SVM-based Receivers.

Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning

Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

Accurate Decoding of Short, Phase-Encoded SSVEPs

Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses

Nonlinear Demodulation and Channel Coding in EBPSK Scheme

Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System

A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics

Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO Systems

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Krein Support Vector Machine Classification of Antimicrobial Peptides

Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Handwritten digit Recognition using Support Vector Machine

Support Vector Machine Model for Currency Crisis Discrimination

Machine Learning Classification can SignificantlyReduce the Cost of Calculating the HamiltonianMatrix in CI Calculations