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26 papers · ranked by Valyu relevance

01
PPubMed6 Jan 2017
SVM and SVM Ensembles in Breast Cancer Prediction SVM and SVM Ensembles in Breast Cancer Prediction
Min-Wei Huang, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke + 2 more
'Chih-Fong Tsai' 'Enrique Hernandez-Lemus'] Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector…
97relevance
02
PPubMed3 Nov 2014
An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis
Ruben Ruiz-Gonzalez, Jaime Gomez-Gil, Francisco Javier Gomez-Gil, Víctor Martínez-Martínez
'Víctor Martínez-Martínez'] The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is…
95relevance
03
PPubMed26 Sep 2006
Support Vector Machine Implementations for Classification & Clustering
Stephen Winters-Hilt, Anil Yelundur, Charlie McChesney, Matthew Landry
'Matthew Landry'] Background We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition.…
94relevance
04
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…
93relevance
05
PPubMed12 Nov 2021
Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?
Ben Lanza, Deepak Parashar
Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of…
91relevance
06
BbioRxiv13 Sep 2018
Multiple-kernel learning for genomic data mining and prediction
Christopher M. Wilson, Kaiqiao Li, Pei-Fen Kuan, Xuefeng Wang
Advances in medical technology have allowed for customized prognosis, diagnosis, and personalized treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for integration of multiple high throughput data sources, however, there are currently no implementations…
91relevance
07
PPubMed1 Nov 2007
SVM clustering
Stephen Winters-Hilt, Sam Merat
Background Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being considered in a number of different ways. Results An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of…
91relevance
08
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…
90relevance
09
PPubMed11 Sep 2014
Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System
Mert Bal, M. Fatih Amasyali, Hayri Sever, Guven Kose + 1 more
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select…
90relevance
10
AarXiv2018
Minimal Support Vector Machine
Shuai Zheng, Chris Ding
Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes. This hyperplane is determined by support vectors. In existing SVM formulations, the objective function uses L2 norm or L1 norm on slack variables. The number of support vectors is a…
90relevance
11
AarXiv2012
Distance Metric Learning for Kernel Machines
Zhixiang Xu, Kilian Q. Weinberger, Olivier Chapelle
Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. Support vector machines (SVM), particularly with RBF kernels, are amongst the most popular classification algorithms that uses distance metrics to compare examples. This paper provides an empirical…
90relevance
12
BbioRxiv17 Jun 2016
Prioritizing 2nd and 3rd order interactions via support vector ranking using sensitivity indices on static Wnt measurements - Part A
Shriprakash Sinha
It is widely known that the sensitivity analysis plays a major role in computing the strength of the influence of involved factors in any phenomena under investigation. When applied to expression profiles of various intra/extracellular factors that form an integral part of a signaling pathway, the variance and density…
89relevance
13
AarXiv2014
An Evaluation of Support Vector Machines as a Pattern Recognition Tool
Eugene Borovikov
- Abstract: The purpose of this report is in examining the generalization performance of Support Vector Machines (SVM) as a tool for pattern recognition and object classification. The work is motivated by the growing popularity of the method that is claimed to guarantee a good generalization performance for the task in…
89relevance
14
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…
87relevance
15
AarXiv2016
A MapReduce-based distributed SVM algorithm for binary classification
Ferhat Özgür Çatak, M. Erdal Balaban
Although Support Vector Machine (SVM) algorithm has a high generalization property to classify for unseen examples after training phase and it has small loss value, the algorithm is not suitable for real-life classification and regression problems. SVMs cannot solve hundreds of thousands examples in training dataset.…
87relevance
16
AarXiv2015
How do the naive Bayes classifier and the Support Vector Machine compare in their ability to forecast the Stock Exchange of Thailand?
Napas Udomsak
—This essay investigates the question of how the naive Bayes classifier and the support vector machine compare in their ability to forecast the Stock Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier is explored. The algorithms are trained using data from the month of January 2010…
87relevance
17
AarXiv2013
CloudSVM : Training an SVM Classifier in Cloud Computing Systems
F. Ozgur Catak, M. Erdal Balaban
In conventional method, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism…
87relevance
18
PPubMed27 Feb 2025
Prediction and Detection of Localised Corrosion Attack of Stainless Steel in Biogas Production: A Machine Learning Classification Approach
María Jesús Jiménez-Come, Francisco Javier González Gallero, Pascual Álvarez Gómez, Victoria Matres + 2 more
'Pascual Álvarez Gómez' 'Victoria Matres' 'Ming Liu' 'Ziyuan Zhao'] Biogas contributes to environmental protection by reducing greenhouse gas emissions and promoting the recycling of organic waste. Its utilization plays a crucial role in addressing the challenges of climate change and sustainability. However, the…
86relevance
19
PPubMed15 Nov 2023
Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model
Noor Ilanie Nordin, Wan Azani Mustafa, Muhamad Safiih Lola, Elissa Nadia Madi + 7 more
'Elissa Nadia Madi' 'Anton Abdulbasah Kamil' 'Marah Doly Nasution' 'Abdul Aziz K. Abdul Hamid' 'Nurul Hila Zainuddin' 'Elayaraja Aruchunan' 'Mohd Tajuddin Abdullah' 'Antonio Brunetti'] Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older…
86relevance
20
BbioRxiv17 Nov 2018
Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database
Sandip S Panesar, Rhett N D’Souza, Fang-Cheng Yeh, Juan C Fernandez-Miranda
Machine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using…
82relevance
21
BbioRxiv14 Aug 2023
Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions
Mohadeseh Zarei Ghoabdi, Elaheh Afsaneh
Quantum machine learning algorithms using the power of quantum computing provide fast- developing approaches for solving complicated problems and speeding-up calculations for big data. As such, they could effectively operate better than the classical algorithms. Herein, we demonstrate for the first time the…
79relevance
22
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…
78relevance
23
CchemRxiv15 Jul 2022
Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes
Souvik Manna, Diptendu Roy, Sandeep Das, Biswarup Pathak
Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important…
72relevance
24
CchemRxiv5 Apr 2024
Computationally predicting the performance of gas sensor arrays for anomaly detection
Paul Morris, Cory Simon
In many gas sensing tasks, we simply wish to become aware of gas compositions that deviate from normal, "business-as-usual" conditions. We provide a methodology, illustrated by example, to computationally predict the performance of a gas sensor array design for detecting anomalous gas compositions. Specifically, we…
71relevance
25
BbioRxiv14 Oct 2024
Transfer Learning Models for Bacterial Strain Dissemination Biomarkers using Weighted Non-Parallel Proximal Support Vector Machines
Ugochukwu O. Ugwu, Richard A. Slayden, Michael Kirby
This paper develops optimization and Machine Learning (ML) algorithms to analyze gene expression datasets from the lungs and spleen of mice, infected intranasally, with two bacterial strains, Francisella tularensis - Schu4 and Live Vaccine Strain (LVS). We propose and utilize Weighted 𝓁_1_-norm Generalized…
71relevance
26
CchemRxiv27 Mar 2024
Machine Learning in Chemistry
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
67relevance

Results for support vector machine

SVM and SVM Ensembles in Breast Cancer Prediction SVM and SVM Ensembles in Breast Cancer Prediction

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

Support Vector Machine Implementations for Classification & Clustering

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

Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?

Multiple-kernel learning for genomic data mining and prediction

SVM clustering

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

Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

Minimal Support Vector Machine

Distance Metric Learning for Kernel Machines

Prioritizing 2nd and 3rd order interactions via support vector ranking using sensitivity indices on static Wnt measurements - Part A

An Evaluation of Support Vector Machines as a Pattern Recognition Tool

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

A MapReduce-based distributed SVM algorithm for binary classification

How do the naive Bayes classifier and the Support Vector Machine compare in their ability to forecast the Stock Exchange of Thailand?

CloudSVM : Training an SVM Classifier in Cloud Computing Systems

Prediction and Detection of Localised Corrosion Attack of Stainless Steel in Biogas Production: A Machine Learning Classification Approach

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions

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

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Computationally predicting the performance of gas sensor arrays for anomaly detection

Transfer Learning Models for Bacterial Strain Dissemination Biomarkers using Weighted Non-Parallel Proximal Support Vector Machines

Machine Learning in Chemistry

SVM and SVM Ensembles in Breast Cancer Prediction SVM and SVM Ensembles in Breast Cancer Prediction

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

Support Vector Machine Implementations for Classification & Clustering

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

Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?

Multiple-kernel learning for genomic data mining and prediction

SVM clustering

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

Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

Minimal Support Vector Machine

Distance Metric Learning for Kernel Machines

Prioritizing 2nd and 3rd order interactions via support vector ranking using sensitivity indices on static Wnt measurements - Part A

An Evaluation of Support Vector Machines as a Pattern Recognition Tool

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

A MapReduce-based distributed SVM algorithm for binary classification

How do the naive Bayes classifier and the Support Vector Machine compare in their ability to forecast the Stock Exchange of Thailand?

CloudSVM : Training an SVM Classifier in Cloud Computing Systems

Prediction and Detection of Localised Corrosion Attack of Stainless Steel in Biogas Production: A Machine Learning Classification Approach

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions

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

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Computationally predicting the performance of gas sensor arrays for anomaly detection

Transfer Learning Models for Bacterial Strain Dissemination Biomarkers using Weighted Non-Parallel Proximal Support Vector Machines

Machine Learning in Chemistry