Search · four archives

Results for Relevance vector machine

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

20 papers · ranked by Valyu relevance

01
AarXiv2013
Variational Relevance Vector Machines
Chris Bishop, Michael E. Tipping
Despite its widespread success, the SVM suffers from some important limitations, one of the most significant being that it makes point predictions rather than generating predictive distributions. Recently Tipping [8] has formulated the Relevance Vector Machine (RVM), a probabilistic model whose functional form is…
93relevance
02
PPubMed10 Mar 2016
Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests
Fan Yang, Wei Sun, Guoyu Lin, Weigong Zhang + 1 more
The scientific and effective prediction of drawbar pull is of great importance in the evaluation of military vehicle trafficability. Nevertheless, the existing prediction models have demonstrated lots of inherent limitations. In this framework, a multiple-kernel relevance vector machine model (MkRVM) including Gaussian…
87relevance
03
AarXiv1 Oct 2018
GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine
Hitoshi Iba, Feng Ji, Hossein Izadi Rad
—This paper proposes a hybrid basis function construction method (GP-RVM) for Symbolic Regression problem, which combines an extended version of Genetic Programming called Kaizen Programming and Relevance Vector Machine to evolve an optimal set of basis functions. Different from traditional evolutionary algorithms…
86relevance
04
AarXiv2020
Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion
Kazuaki Murayama, Shuichi Kawano
In the variational relevance vector machine, the gamma distribution is representative as a hyperprior over the noise precision of automatic relevance determination prior. Instead of the gamma hyperprior, we propose to use the inverse gamma hyperprior with a shape parameter close to zero and a scale parameter not…
84relevance
05
AarXiv2013
A comparison of SVM and RVM for Document Classification
Muhammad Rafi, M. Shahid Shaikh
Document classification is a task of assigning a new unclassified document to one of the predefined set of classes. The content based document classification uses the content of the document with some weighting criteria to assign it to one of the predefined classes. It is a major task in library science, electronic…
79relevance
06
AarXiv2019
Proposing a Localized Relevance Vector Machine for Pattern Classification
Farhood Rismanchian, Karim Rahimian
—Relevance vector machine (RVM) can be seen as a probabilistic version of support vector machines which is able to produce sparse solutions by linearly weighting a small number of basis functions instead using all of them. Regardless of a few merits of RVM such as giving probabilistic predictions and relax of parameter…
78relevance
07
PPubMed19 Mar 2010
Kernel regression for fMRI pattern prediction
Carlton Chu, Yizhao Ni, Geoffrey Tan, Craig J. Saunders + 1 more
'John Ashburner'] This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial…
77relevance
08
PPubMed16 Oct 2023
Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm
Yuzhen Su, Weichuan Ye, Kai Yang, Meng Li + 2 more
'Qingtai Xiao'] Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed…
75relevance
09
PPubMed16 Apr 2010
Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS
Hwanjo Yu, Taehoon Kim, Jinoh Oh, Ilhwan Ko + 2 more
'Wook-Shin Han'] Background Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by…
68relevance
10
PPubMed10 Dec 2012
Your Relevance Feedback Is Essential: Enhancing the Learning to Rank Using the Virtual Feature Based Logistic Regression Enhancing the Learning to Rank Using the VFLR
Fei Cai, Deke Guo, Honghui Chen, Zhen Shu + 1 more
Information retrieval applications have to publish their output in the form of ranked lists. Such a requirement motivates researchers to develop methods that can automatically learn effective ranking models. Many existing methods usually perform analysis on multidimensional features of query-document pairs directly and…
68relevance
11
PPubMed30 Mar 2023
Distributionally robust learning-to-rank under the Wasserstein metric Distributionally robust learning-to-rank under the Wasserstein metric
Shahabeddin Sotudian, Ruidi Chen, Ioannis Ch. Paschalidis, Kathiravan Srinivasan
Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional data shift, and malicious adversaries who wish to degrade the…
66relevance
12
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…
63relevance
13
AarXiv2018
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
Tatsuhiro Aoshima, Kei Kobayashi, Mihoko Minami
Machine learning has played an important role in information retrieval (IR) in recent times. In search engines, for example, query keywords are accepted and documents are returned in order of relevance to the given query; this can be cast as a multi-label ranking problem in machine learning. Generally, the number of…
63relevance
14
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…
62relevance
15
BbioRxiv7 Nov 2018
Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings
Maxat Kulmanov, Senay Kafkas, Andreas Karwath, Alexander Malic + 3 more
Recent developments in machine learning have lead to a rise of large number of methods for extracting features from structured data. The features are represented as a vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities…
59relevance
16
BbioRxiv31 Jul 2025
SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation
Jack Freeman, Robert J. Millikin, Leo Xu, Ishaan Sharma + 5 more
Generating and testing hypotheses is a critical aspect of biomedical science. Typically, researchers generate hypotheses by carefully analyzing available information and making logical connections, which are then tested. The accelerating growth of biomedical literature makes it increasingly difficult to keep pace with…
58relevance
17
BbioRxiv15 Aug 2021
Pairwise Relative Distance (PRED) is an intuitive and robust metric for assessing vector similarity and class separability
Aarush Mohit Mittal, Andrew C. Lin, Nitin Gupta
Scientific studies often require assessment of similarity between ordered sets of values. Each set, containing one value for every dimension or class of data, can be conveniently represented as a vector. The commonly used metrics for vector similarity include angle-based metrics, such as cosine similarity or Pearson…
58relevance
18
BbioRxiv31 Jul 2025
SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation
Jack Freeman, Robert J. Millikin, Leo Xu, Ishaan Sharma + 6 more
Generating and testing hypotheses is a critical aspect of biomedical science. Typically, researchers generate hypotheses by carefully analyzing available information and making logical connections, which are then tested. The accelerating growth of biomedical literature makes it increasingly difficult to keep pace with…
58relevance
19
CchemRxiv25 Aug 2025
The AI Revolution in Chemistry: Shaping the Future of Materials and Biomedical Sciences
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
54relevance
20
CchemRxiv15 Apr 2025
Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases
Authors not listed
Compound similarity is fundamental to various cheminformatics analyses, particularly in the drug discovery industry, where the structure-activity principle is central to medicinal chemistry. Historically, binary fingerprints combined with Tanimoto and “Tanimoto-related metrics” (such as Dice, Sørensen–Dice, and…
53relevance

Results for Relevance vector machine

Variational Relevance Vector Machines

Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests

GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine

Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion

A comparison of SVM and RVM for Document Classification

Proposing a Localized Relevance Vector Machine for Pattern Classification

Kernel regression for fMRI pattern prediction

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

Your Relevance Feedback Is Essential: Enhancing the Learning to Rank Using the Virtual Feature Based Logistic Regression Enhancing the Learning to Rank Using the VFLR

Distributionally robust learning-to-rank under the Wasserstein metric Distributionally robust learning-to-rank under the Wasserstein metric

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

Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification

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

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

Pairwise Relative Distance (PRED) is an intuitive and robust metric for assessing vector similarity and class separability

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

The AI Revolution in Chemistry: Shaping the Future of Materials and Biomedical Sciences

Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases

Variational Relevance Vector Machines

Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests

GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine

Relevance Vector Machine with Weakly Informative Hyperprior and Extended Predictive Information Criterion

A comparison of SVM and RVM for Document Classification

Proposing a Localized Relevance Vector Machine for Pattern Classification

Kernel regression for fMRI pattern prediction

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

Your Relevance Feedback Is Essential: Enhancing the Learning to Rank Using the Virtual Feature Based Logistic Regression Enhancing the Learning to Rank Using the VFLR

Distributionally robust learning-to-rank under the Wasserstein metric Distributionally robust learning-to-rank under the Wasserstein metric

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

Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification

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

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

Pairwise Relative Distance (PRED) is an intuitive and robust metric for assessing vector similarity and class separability

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

The AI Revolution in Chemistry: Shaping the Future of Materials and Biomedical Sciences

Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases