On the public citation graph

Reducing the classification cost of support vector classifiers through an ROC-based reject rule

https://doi.org/10.1007/s10044-004-0209-2
0 signed · 48 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-44522-6_63openalex-snapshot
An Optimal Reject Rule for Binary Classifiers
inferredcites →https://doi.org/10.1007/3-540-45665-1_6openalex-snapshot
Support Vector Machines with Embedded Reject Option
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot
Support-Vector Networks
inferredcites →https://doi.org/10.1007/pl00013827openalex-snapshot
Classification Accuracy Based on Observed Margin
inferredcites →https://doi.org/10.1016/0031-3203(93)90097-gopenalex-snapshot
A statistical decision rule with incomplete knowledge about classes
inferredcites →https://doi.org/10.1016/s0031-3203(96)00142-2openalex-snapshot
The use of the area under the ROC curve in the evaluation of machine learning algorithms
inferredcites →https://doi.org/10.1016/s0031-3203(97)00056-3openalex-snapshot
Classifier design with incomplete knowledge
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
inferredcites →https://doi.org/10.1023/a:1007601015854openalex-snapshot
Robust Classification for Imprecise Environments
inferredcites →https://doi.org/10.1080/00224065.2000.11980009openalex-snapshot
Probability and Statistics for Engineers and Scientists, 6th Ed.
inferredcites →https://doi.org/10.1093/clinchem/39.4.561openalex-snapshot
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine
inferredcites →https://doi.org/10.1097/00004424-198609000-00009openalex-snapshot
ROC Methodology in Radiologic Imaging
inferredcites →https://doi.org/10.1109/72.410358openalex-snapshot
A method for improving classification reliability of multilayer perceptrons
inferredcites →https://doi.org/10.1109/72.788642openalex-snapshot
Moderating the outputs of support vector machine classifiers
inferredcites →https://doi.org/10.1109/tec.1957.5222035openalex-snapshot
An optimum character recognition system using decision functions
inferredcites →https://doi.org/10.1109/tit.1970.1054406openalex-snapshot
On optimum recognition error and reject tradeoff
inferredcites →https://doi.org/10.17877/de290r-14262openalex-snapshot
Making Large-Scale SVM Learning Practical
inferredcites →https://doi.org/10.2307/2284195openalex-snapshot
Probability and Statistics for Engineers and Scientists.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.5281/zenodo.1083044openalex-extracted
A Kernel Based Rejection Method For Supervised Classification
inferred← citeshttps://doi.org/10.1002/ente.202201033openalex-snapshot
Fault Diagnosis of Electric Submersible Pumps Using a Three‐Stage Multiscale Feature Transformation Combined with CNN–SVM
inferred← citeshttps://doi.org/10.1007/978-3-031-90095-2_32openalex-snapshot
The Ellipsoidal Separation Machine
inferred← citeshttps://doi.org/10.1007/978-3-540-73499-4_5openalex-snapshot
An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules
inferred← citeshttps://doi.org/10.1007/978-3-642-24085-0_13openalex-snapshot
Shaping the Error-Reject Curve of Error Correcting Output Coding Systems
inferred← citeshttps://doi.org/10.1007/s10044-008-0104-3openalex-snapshot
Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images
inferred← citeshttps://doi.org/10.1007/s10994-007-5013-yopenalex-snapshot
On the use of ROC analysis for the optimization of abstaining classifiers
inferred← citeshttps://doi.org/10.1007/s10994-024-06534-xopenalex-snapshot
Machine learning with a reject option: a survey
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106762openalex-snapshot
Dual objective bounded abstaining model to control performance for safety-critical applications
inferred← citeshttps://doi.org/10.1016/j.jfranklin.2017.05.003openalex-snapshot
Twin SVM with a reject option through ROC curve
inferred← citeshttps://doi.org/10.1016/j.neucom.2019.04.047openalex-snapshot
BA2Cs: Bounded abstaining with two constraints of reject rates in binary classification
inferred← citeshttps://doi.org/10.1016/j.patcog.2008.03.019openalex-snapshot
Filtering segmentation cuts for digit string recognition
inferred← citeshttps://doi.org/10.1016/j.patcog.2011.08.001openalex-snapshot
Design of reject rules for ECOC classification systems
inferred← citeshttps://doi.org/10.1016/j.patcog.2016.10.011openalex-snapshot
Performance measures for classification systems with rejection
inferred← citeshttps://doi.org/10.1016/j.patrec.2005.10.015openalex-snapshot
The interaction between classification and reject performance for distance-based reject-option classifiers
inferred← citeshttps://doi.org/10.1016/j.patrec.2008.02.025openalex-snapshot
General solution and learning method for binary classification with performance constraints
inferred← citeshttps://doi.org/10.1109/icdmw.2013.45openalex-snapshot
Cost-Free Learning for Support Vector Machines with a Reject Option
inferred← citeshttps://doi.org/10.1109/inista.2018.8466266openalex-snapshot
Real-Time Event Detection in Time-Series Classification Based on Amplitude Rejection
inferred← citeshttps://doi.org/10.1109/tkde.2014.2312336openalex-snapshot
A New Strategy of Cost-Free Learning in the Class Imbalance Problem
inferred← citeshttps://doi.org/10.1109/tpami.2008.239openalex-snapshot
Optimal Decision Rule with Class-Selective Rejection and Performance Constraints
inferred← citeshttps://doi.org/10.1142/s0218001412500012openalex-snapshot
HEARTBEAT CLASSIFICATION USING SUPPORT VECTOR MACHINES (SVMs) WITH AN EMBEDDED REJECT OPTION
inferred← citeshttps://doi.org/10.1186/s12911-021-01655-yopenalex-snapshot
Machine learning with asymmetric abstention for biomedical decision-making
inferred← citeshttps://doi.org/10.3233/aic-130566openalex-snapshot
The data replication method for the classification with reject option
inferred← citeshttps://doi.org/10.3233/ida-2009-0354openalex-snapshot
The ROC isometrics approach to construct reliable classifiers
inferred← citeshttps://doi.org/10.32657/10220/47324openalex-snapshot
Robust classification and detection with applications in biomedical images
inferred← citeshttps://doi.org/10.34768/amcs-2020-0025openalex-snapshot
Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
inferred← citeshttps://doi.org/10.5120/842-1141openalex-snapshot
A Neural Network-Based Method for Brain Abnormality Detection in MR Images Using Zernike Moments and Geometric Moments
inferred← citeshttps://doi.org/10.5555/1390681.1442792openalex-snapshot
Classification with a Reject Option using a Hinge Loss
inferred← citeshttps://doi.org/10.6084/m9.figshare.16880413openalex-snapshot
Additional file 1 of Machine learning with asymmetric abstention for biomedical decision-making
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.