Library · Machine learning & AI
What Support Vector Machines got wrong, from 60 theses
Summary written by a model from the records below: The records document numerous empirical settings where Support Vector Machines and regression variants failed, were beaten by alternative models, or were rejected during system design. Across these studies, performance issues arose from computational bottlenecks on large datasets, difficulties handling imbalanced or complex data, and competition from simpler linear baselines or modern neural and tree architectures. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 4 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Support vector machines are often outperformed by tree ensembles and neural networks
Researchers found that support vector machines lagged behind random forests, boosting, and multilayer perceptrons across various classification and regression tasks. Deep neural architectures and tree ensembles proved superior at processing raw images, sequential features, and complex high dimensional tabular inputs.
Tried and failed
support vector machine classification applied to tabular delay classification. Outcome: worse than baseline. Reason: non-parametric model struggled compared to parametric deep neural network architectures
Novel deep learning approaches for flight delays prediction · Cranfield · PhD
Tried and failed
Support vector machine classification applied to metabolomics-based disease risk prediction. Outcome: worse than baseline. Reason: SVM had substantially lower predictive AUC compared to tree-based ensemble models
Eating Behaviors in Adolescence and Young Adulthood and Adult Cardiometabolic Disease Risk · Harvard · PhD
Lost to a baseline
Support vector machines (SVM) and logistic regression were outperformed by Random Forest when trained on human protein target abundances to separate case and control patients across metagenomic disease cohorts
PREDICTION OF BACTERIAL HORIZONTAL GENE TRANSFER AND DISEASE-RELEVANT HOST-MICROBIOME PROTEIN- PROTEIN INTERACTIONS · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected Support Vector Machines and Logistic Regression in favor of Random Forest for binary visibility classification
Development of a machine vision system to estimate the physical attributes of potato tubers on-the-go at the post-harvest stage · Dalhousie · Master's
Lost to a baseline
Support Vector Machines (SVMs) underperformed on training cross-validation (accuracy 0.678) compared to Random Forests (0.932) and Naive Bayes (0.792).
Lost to a baseline
Support Vector Classifier (65.77% raw, 55.28% summary/Fourier/Wavelet) lost to untuned Random Forest (80.28% raw, 76.91% summary, 79.63% Fourier, 79.80% Wavelet) across all malware dataset representations.
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic View · Johns Hopkins · Master's
Considered and rejected
Considered and rejected: Support vector machines (SVM) with Generative Adversarial Active Learning (GAAL) were rejected because SVM performs poorly on complex image classification tasks.
Engineering-Driven Learning Approaches for Bio-Manufacturing and Personalized Medicine · Georgia Tech · PhD
Lost to a baseline
Support Vector Machine (SVM) with PCA feature reduction achieved substantially lower intent classification accuracy across all time steps compared to the Long Short-Term Memory (LSTM) deep learning network.
Intent sensing for assistive technology · Oxford · PhD
Tried and failed
Linear support vector classification applied to time-series image encodings. Outcome: worse than baseline. Reason: Failed to capture non-linear relationships across multi-channel image representations compared to convolutional models
Optimizing High-Performance Computing Systems: Insights from System Monitoring, Workload Management, and Scheduling Strategies · Texas Tech · PhD
Considered and rejected
Considered and rejected: Traditional machine learning models (Support Vector Regression and Random Forests) were rejected as the primary architecture for short-term arrival traffic forecasting in favor of recurrent neural networks (LSTMs) due to their inability to directly capture multi-scale sequential dependencies without manual lag feature engineering.
Predictive Modeling of Aircraft Arrival Times in the Terminal Maneuvering Area Through Data-Driven Techniques · Georgia Tech · PhD
Tried and failed
Support vector machines with high-dimensional descriptors applied to molecular formulation and glass forming classification. Outcome: worse than baseline. Reason: High feature dimensionality and noise sensitivity degraded classification performance relative to tree ensembles
Overcoming challenges of solid dosage formulation development by using emerging technologies · UT Austin · PhD
Lost to a baseline
Support Vector Machine (SVM), Random Forests, and Gaussian Processes achieved lower test dataset R2 compared to the 2-hidden-layer ANN model for predicting HFRC tensile stress-strain behavior.
Condition Assessment of Civil Infrastructure and Materials Using Deep Learning · Virginia Tech · Doctorate
Lost to a baseline
Partial least-squares regression (test R² = 0.81) and support vector regression (test R² = 0.86) were beaten by Multilayer Perceptron Regression (test R² = 0.96) when predicting 1-cm SOC from 10-cm trained hyperspectral models.
High-resolution & high-throughput methods for soil carbon research · University College Dublin · PhD
High computational demands and memory limits hinder scaling on large datasets
Practitioners encountered prohibitive training times, slow inference, and out of memory crashes when executing support vector algorithms on large datasets. These scaling bottlenecks and high dimensional distance matrix computations frequently led authors to abandon the method for faster alternatives.
Tried and failed
Support Vector Machine classification applied to multivariate time series telemetry anomaly detection. Outcome: worse than baseline. Reason: exhibited lowest classification accuracy and highest training time compared to ensemble and linear baselines
Machine Learning Based Spectrum Fingerprinting of Drones for Defensive Cyber Operations · Harvard · Master's
Tried and failed
Support vector regression applied to traffic signal preemption optimization. Outcome: did not generalise. Reason: High feature dimensionality relative to scenario sample count alongside high training times
Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected Support Vector Machines (SVM) for core image classification due to exponential computational scaling bottlenecks on large datasets.
Application of Computational Methods to Data Integration and Geoscientific Problems in Mineral Exploration and Mining · Queen's University · PhD
Considered and rejected
Considered and rejected: Rejected Support Vector Machine (SVM) for subsequent trials due to multi-hour runtimes, high compute requirements, and negligible accuracy gains over RF
Evaluating Multifrequency SAR for Crop Classification in Saskatchewan, Canada and Georgia, USA · Carleton University · Master's
Tried and failed
support vector machine on minimal frequency features applied to multi-class vibration fault diagnostics. Outcome: worse than baseline. Reason: insufficient feature dimensionality caused poor class separation and extreme training times
PROGNOSTICS AND HEALTH MANAGEMENT OF WIND TURBINE GEARBOXES: DETECTION AND DIAGNOSTICS USING MACHINE LEARNING · West Texas A&M University · Master's
Considered and rejected
Considered and rejected: Support Vector Machines (SVM) for load acceptance classification due to intractable run times on large transaction datasets.
Alternative Freight Contracts: Data-driven Design Under Uncertainty · MIT · PhD
Tried and failed
Support Vector Regression applied to tabular spatial event regression. Outcome: worse than baseline. Reason: yielded high inference execution times and worse RMSE metrics than ensemble and neural baselines
Human sensor networks for natural disasters · Imperial · PhD
Tried and failed
kernel support vector regression applied to high-dimensional genomic drug response prediction. Outcome: infeasible cost. Reason: distance kernel matrix computation caused out-of-memory crashes as feature dimensionality increased
Representation of features as images with neighborhood dependencies for improvement of anti-cancer drug sensitivity predictive modeling using machine learning · Texas Tech · PhD
Tried and failed
Support Vector Regression applied to pixelwise quantitative parameter mapping. Outcome: too slow. Reason: Poor computational scaling with large dataset sizes led to excessive training and inference times
Simultaneous morphological and quantitative imaging of cartilage using phase-cycled bSSFP · Imperial · Doctorate
Tried and failed
KNN and support vector regression applied to high-dimensional spatiotemporal inference. Outcome: worse than baseline. Reason: poor scaling and performance when handling high-dimensional multi-scale spatial and temporal features
Urban air pollution modelling with machine learning using fixed and mobile sensors · Imperial · PhD
Class imbalance causes severe prediction bias and uncalibrated output probabilities
When applied to imbalanced data, support vector classifiers consistently biased predictions toward the majority class, resulting in near zero recall, low sensitivity, and poor specificity. Furthermore, models were rejected because standard support vector formulations fail to output directly calibrated probabilities for risk scoring.
Tried and failed
support vector machines for classification applied to drug approval prediction. Outcome: worse than baseline. Reason: poor probability calibration led to the worst out-of-sample performance
Analytics for accelerating biomedical innovation · MIT · PhD
Tried and failed
support vector machine for binary classification applied to imbalanced time-series trace classification. Outcome: worse than baseline. Reason: failed to handle class imbalance compared to boosting methods with random undersampling
Hardware-level Vulnerabilities and Support for Secure and Safe Cyber-Physical Systems · Cornell · PhD
Tried and failed
support vector machine with metaheuristic feature selection applied to imbalanced genomic biomarker classification. Outcome: did not generalise. Reason: severe class imbalance degraded classification performance on minority classes
UNCERTAINTY MITIGATION IN IMAGE-BASED MACHINE LEARNING MODELS FOR PRECISION MEDICINE · Georgia Tech · PhD
Lost to a baseline
Support Vector Machines with RBF kernels failed to exceed the random baseline (87.1%) on the imbalanced healthy vs. gait disorder (N/GD) binary classification task (-0.8% and -3.8% below baseline for z-score and min-max normalization, respectively).
Human gait analysis : machine learning-based classification of gait disorders · TU Wien · PhD
Considered and rejected
Considered and rejected: Rejected Support Vector Machines and K-Nearest Neighbor algorithms because they overfit to the majority upland class and severely underestimated peatland extent
The Classification and Characterization of Canadian Boreal Peatland Sub-classes · Carleton University · Master's
Considered and rejected
Considered and rejected: Rejected Support Vector Machine (SVM) for Stage-1 due to poor sensitivity (70.30%), lack of interpretability, and feature scaling requirements
Decentralized Processing Techniques for Biomedical Signal Classification · University College Dublin · PhD
Tried and failed
multiple instance learning support vector machine applied to instance-level credibility prediction. Outcome: worse than baseline. Reason: predicted all instances as negative, yielding zero precision and recall
Anomalous Information Detection in Social Media · Virginia Tech · Doctorate
Tried and failed
Support Vector Classifier for event detection applied to traffic delay occurrence classification. Outcome: worse than baseline. Reason: SVC suffered from very low recall and detection rates for positive instances
Considered and rejected
Considered and rejected: Rejected using Support Vector Machines (SVM) to learn prior probabilities in AttriInfer because SVM does not directly produce calibrated probabilities.
Privacy Protection via Adversarial Examples · Duke · PhD
Simpler linear models frequently beat support vector algorithms
Standard logistic regression, regularized linear models, and discriminant analysis routinely matched or exceeded the predictive accuracy of support vector machines. Authors repeatedly preferred these simpler baselines because they offered greater transparency, physical interpretability, and lower risk of overfitting on limited sample sizes.
Tried and failed
support vector machines instead of logistic regression applied to human task performance prediction. Outcome: worse than baseline. Reason: limited sample size led to risk of overfitting without performance gains
Measuring and predicting detection performance on security images as a function of image quality · UT Austin · PhD
Lost to a baseline
Support Vector Machine (SVM) and machine learning models were noted in cited literature (Beutel et al., 2019) to be outperformed by standard logit models in systemic crisis early-warning systems.
Išvestinių finansinių priemonių poveikio Euro zonos šalių sisteminei rizikai vertinimas Evaluation of Financial Derivatives Impact on Systemic Risk of Euro Area Countries · Mykolas Romeris University · PhD
Lost to a baseline
Support Vector Machine (SVM, 74.40% emergence, 84.34% stem elongation) and Random Forest (70.19% emergence, 82.48% stem elongation) were beaten by Logistic Regression (74.59% emergence, 84.40% stem elongation) on central RGB features.
Spatial Data Analysis in Digital Agriculture Application on Crops Growth Monitoring · University College Dublin · PhD
Lost to a baseline
Support Vector Machine (SVM) on original ORL data achieved 83.9% accuracy, which was outperformed by Quadratic Discriminant (QD) on original ORL data (91.5%).
Higher order tensor decompositions: from intuition to implementation and application · Imperial · PhD
Lost to a baseline
Logistic Regression ML model achieved a higher true positive rate (61.11%) than Support Vector Machine (27.78%) and Random Forest (33.33%) on the ALK mutation dataset.
Protein Dynamics in Mutated Kinases · Penn · PhD
Lost to a baseline
Support vector machine predicting gene expression from regulatory components achieved R2 = 0.073, performing worse than multiple linear regression (R2 = 0.08985).
Transcript cleavage and polyadenylation in plants · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected support vector machines for dictionary feature selection after cross-validation showed multinomial lasso performed better.
Value Representation · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected the SVM (Support Vector Machine) model for the empirical evaluation of derivatives' impact on systemic risk because its parameters are difficult to interpret, it requires constraints/assumptions similar to simulation models, and it is less effective than logit regression.
Išvestinių finansinių priemonių poveikio Euro zonos šalių sisteminei rizikai vertinimas Evaluation of Financial Derivatives Impact on Systemic Risk of Euro Area Countries · Mykolas Romeris University · PhD
Considered and rejected
Considered and rejected: Rejected Ridge, Lasso, Support Vector Regression (SVR), and Logistic Regression for area-volume curve fitting in favor of multilinear regression to preserve simplicity, interpretability, and physical consistency.
From Sources to Sinks: Advancing Surface Water Management Through Satellite Remote Sensing · University of Washington · PhD
Models fail to generalize across domain shifts and operational changes
Support vector models struggled to maintain predictive accuracy when exposed to open set conditions, different system operational configurations, or real world measurement variations. These methods exhibited low base classifier diversity in ensembles, overfit to design artifacts, and generated false alarms under nominal conditions.
Tried and failed
support vector machine for pattern classification applied to fault classification across system configurations. Outcome: did not generalise. Reason: Model failed to generalize to different system operational configurations without larger training sets.
Protection and Cybersecurity in Inverter-Based Microgrids · Virginia Tech · Doctorate
Tried and failed
support vector regression on multiscale genomic windows applied to predicting repressive chromatin mark signals. Outcome: did not generalise. Reason: models failed to predict H3K9me3 distribution across validation ChIP-seq and CUT&RUN datasets
THE MOLECULAR INTERPLAY AND EVOLUTION OF PROMOTER-PROXIMAL PAUSING AND ITS RELATIONSHIP TO HISTONE MODIFICATIONS · Cornell · PhD
Tried and failed
bagging SVM ensembles for uncertainty estimation applied to out-of-distribution workload detection. Outcome: did not generalise. Reason: insufficient base-classifier diversity due to convex optimization of support vector machines
Trustworthy Binary Classifications in Dynamic Systems Under Uncertainty · Georgia Tech · PhD
Tried and failed
support vector regression for spectral super-resolution applied to hyperspectral image reconstruction. Outcome: did not generalise. Reason: introduced high-frequency noise and failed to generalize from synthetic shifts to real-world optical measurements
Computational approaches for sub-meter ocean color remote sensing · MIT · PhD
Tried and failed
deep support vector data description applied to cross-domain open-set fault diagnosis. Outcome: did not generalise. Reason: struggled to delineate effective decision boundaries across domain shifts and open-set conditions
Development of deep meta-learning framework for cross-domain multisensory systems · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected classic radial basis function (RBF) and support vector machine (SVM) base models in CTS meta-modeling due to dataset overfitting/bias and poor generalizability to unseen designs.
Machine Learning in Physical Design for 2D and 3D Integrated Circuits · Georgia Tech · PhD
Tried and failed
support vector machine for anomaly detection applied to multivariate time series fault diagnosis. Reason: produced false positives under nominal operating conditions, incorrectly triggering mitigation procedures
A Methodology for the Selection and Integration of Self-Healing Architectures in Human Habitation Design · Georgia Tech · PhD
Tried and failed
support vector regression for spectral super-resolution applied to hyperspectral reconstruction from spatially oversampled imagery. Outcome: did not generalise. Reason: failed to reconstruct mid-to-high frequency spectral features under optical tilt and real-world filter variations
Computational approaches for sub-meter ocean color remote sensing · MIT · PhD
Complex or misconfigured kernels cause severe overfitting and extreme errors
Utilizing non-linear options such as sigmoid, high-degree polynomial, or radial basis function kernels frequently induced severe overfitting and extreme predictive errors compared to baselines. Authors observed that high-degree polynomial boundaries and unaligned kernel transformations suffered from elevated variance and failed to benefit from retraining.
Tried and failed
support vector machine with sigmoid kernel applied to spectroscopic regression and classification. Outcome: did not generalise. Reason: poor predictive performance and failure to validate on microbial count and classification tasks
Sensor Approaches for the Non-Destructive Assessment of Food Safety and Authenticity · Cranfield · PhD
Tried and failed
support vector regression with RBF kernel applied to recurrent genomic selection. Outcome: worse than baseline. Reason: produced least effective selection response, accelerated genetic variance loss, and failed to benefit from retraining
Computational studies on recurrent genomic selection for genetic improvement of soybeans · Iowa State · PhD
Tried and failed
high upsampling rates in support vector regression applied to digital predistortion for power amplifiers. Outcome: overfit. Reason: Excessive sampling redundancy led to overfitting and degraded worst-case error vector magnitude performance.
Traversing the Volterra series for digital predistortion applications · Iowa State · PhD
Tried and failed
support vector regression with polynomial feature augmentation applied to tabular prediction with non-linear features. Outcome: worse than baseline. Reason: adding polynomial and interaction terms increased prediction error compared to simple linear baselines
Unifying Strategic Military Force Design and Operational Warfighting: A Stochastic Game Approach · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected nonlinear kernels for Support Vector Machines (SVMs) in favor of a linear kernel to avoid overfitting models and poor performance.
Resolution and Reliability in Functional Connectivity Analysis · Johns Hopkins · Master's
Tried and failed
support vector machine with cubic kernel applied to Raman spectral classification. Outcome: overfit. Reason: High-degree polynomial decision boundary led to severe overfitting and high cross-validation variance on spectral data
Nanobiotechnology Enabled Environmental Sensing of Water and Wastewater · Virginia Tech · Doctorate
Hyperplanes and continuous formulations struggle with non-linear dynamics and discrete targets
Support vector formulations failed to model non-linear boundaries, sequential temporal patterns, and sensor interactions effectively. In addition, support vector regression performed poorly when applied to discrete count data because its formulation assumes continuous response variables.
Tried and failed
linear support vector machines applied to tabular loan default classification. Outcome: worse than baseline. Reason: linear decision boundaries failed to capture non-linear feature interactions across retail banking datasets
Modelling the probability of household default for conventional and Islamic banking. · Cranfield · PhD
Tried and failed
support vector regression surrogate modeling applied to reactor multiphysics simulation dataset. Outcome: worse than baseline. Reason: systematically underperformed and unable to accurately capture complex variations in the dataset
Development of Coupled Machine Learning and Optimization Framework for Comprehensive Molten Salt Reactor Design · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected Support Vector Machines and logistic regression for frequency security boundary extraction due to poor accuracy from simple hyperplanes on nonlinear high-dimensional boundaries.
Data-driven stability-constrained optimisation for software-defined power systems with high IBR penetration · Imperial · PhD
Tried and failed
support vector regression with linear kernel applied to photovoltaic power generation forecasting. Outcome: worse than baseline. Reason: linear kernel cannot capture nonlinear relationships in the data
A Coordinated Voltage Management Method Utilizing Battery Energy Storage Systems and Smart PV Inverters in Distribution Networks with High PV and Wind Penetrations · Virginia Tech · Doctorate
Tried and failed
Support Vector Regression applied to discrete count data prediction. Outcome: worse than baseline. Reason: assumes continuous response variables rather than discrete count distributions
Crash modification factors, predictive hotspot identification, and treatment selection in Texas and peer states · UT Austin · PhD
Tried and failed
support vector machine with RBF kernel applied to time series trace classification. Outcome: no signal. Reason: Kernel SVM failed to capture temporal dynamics in high-dimensional sequential traces for multiclass classification
Ghost dog: The role of dynamic frequency monitoring in application and anomaly detection on Intel microarchitectures · Iowa State · Master's
Tried and failed
support vector regression applied to optical sensor pressure estimation. Outcome: worse than baseline. Reason: struggled to model complex non-linear mappings from unaligned multi-trace sensor interactions
PHYSICAL INTELLIGENCE ENABLED BY SOFT ROBOTIC SYSTEMS · Cornell · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Develop multi-class support vector machine methods using surrogate loss to simultaneously estimate minimal clinically important differences for improvement and worsening. Blocker: None
An Estimation and Inferential Framework for Minimal Clinically Important Difference · University at Buffalo · PhD
Left open
Train and evaluate Support Vector Machines and Gradient Boosting Machines on FEA-derived temperature history features to predict DED residual stresses. Blocker: Requires the proprietary SYSWELD/ANSYS FEA simulation models or the author's private dataset of simulated temperature histories and stresses
Residual stress in metal additive manufacturing of thin-walled components: investigation and development of prediction models with respect to path planning · University of Windsor · PhD
Left open
Develop improved Support Vector Machine algorithms for doubly censored survival data. Blocker: No specific formulation or concrete algorithmic approach is provided beyond a high-level suggestion.
Survival Tree Models and Survival Ensemble Methods in Machine Learning · University of Camerino · PhD
Left open
Develop an optimal engine oil emission experimental testing protocol using one-class support vector machines (OCSVM). Blocker: Requires experimental engine oil emission datasets or test bench access
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Valdas Grigonis · Mykolas Romeris University · PhD · 2021
Išvestinių finansinių priemonių poveikio Euro zonos šalių sisteminei rizikai vertinimas Evaluation of Financial Derivatives Impact on Systemic Risk of Euro Area Countries - Ryan Edward. O'Shea · MIT · PhD · 2021
Computational approaches for sub-meter ocean color remote sensing - Shahzaib Khan · University of Washington · PhD · 2026
From Sources to Sinks: Advancing Surface Water Management Through Satellite Remote Sensing - Hyungu Choi · Georgia Tech · PhD · 2025
Predictive Modeling of Aircraft Arrival Times in the Terminal Maneuvering Area Through Data-Driven Techniques - Marc Anthony Koerschner · Georgia Tech · PhD · 2025
A Methodology for the Selection and Integration of Self-Healing Architectures in Human Habitation Design - Krithika Balaji · Imperial · Doctorate · 2025
Simultaneous morphological and quantitative imaging of cartilage using phase-cycled bSSFP - Jacob Lever · Imperial · PhD · 2025
Human sensor networks for natural disasters - Qian Chen · Imperial · PhD · 2025
Data-driven stability-constrained optimisation for software-defined power systems with high IBR penetration - Shayan Kabiri · University College Dublin · PhD · 2025
High-resolution & high-throughput methods for soil carbon research - Yunan Li · University College Dublin · PhD · 2025
Spatial Data Analysis in Digital Agriculture Application on Crops Growth Monitoring - Samuel Heffer · Cranfield · PhD · 2025
Sensor Approaches for the Non-Destructive Assessment of Food Safety and Authenticity - Elijah Joshua Barker · Iowa State · Master's · 2025
Ghost dog: The role of dynamic frequency monitoring in application and anomaly detection on Intel microarchitectures
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