A new HL-SVR approach handles unequal sample sizes in SVR for engineering data modeling.
problem SVR assumes equal sample sizes, but unequal sizes are common in engineering.
method HL-SVR combines low-level SVR for larger samples and high-level SVR for smaller samples.
result HL-SVR produces more accurate predictions than conventional SVR.
SVR analyzed within RQ framework for risk management.
problem Risk management in stochastic optimization.
method Risk Quadrangle (RQ) theory applied to SVR.
result SVR formulations as minimization of Vapnik error and CVaR norm.
BOA-SVR model improves SVR performance in stock market forecasting.
problem Optimizing SVR parameters for better stock market forecasting accuracy.
method A novel BOA-SVR model using Butterfly Optimization Algorithm.
result The BOA-SVR model outperforms other meta-heuristic algorithms in stock market forecasting.
This paper proposes MMD-SVR to improve SVR's margin distribution for better generalization.
problem Improving SVR's generalization performance by maximizing the margin distribution of the whole dataset.
method Introducing MMD-SVR with coupled constraints to convert a non-convex optimization problem into a convex one.
result MMD-SVR significantly improves prediction accuracy and generalization compared to classic SVR.
SVR-Tree improves classification trees for imbalanced and sparse data.
problem Classification difficulties in imbalanced and sparse data.
method Proposes SVR-Tree, penalizing the Surface-to-Volume Ratio of decision sets.
result SVR-Tree improves generalization error compared to other imbalance algorithms.
LS-SVR and Bayesian RBF networks are shown to be theoretically similar.
problem Improving LS-SVR performance through Bayesian methods.
method Formal demonstration of theoretical similarities between LS-SVR and Bayesian RBF networks.
result LS-SVR and Bayesian RBF networks have equivalent theoretical expressions.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
problem Inaccurate long-term stock price predictions.
method Adaptive Weighted Genetic Algorithm-Optimized SVR (IGA-SVR).
result Reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR.
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
Develops Co_SVR for multi-fidelity modeling combining HF and LF models.
problem Combining high-fidelity and low-fidelity models for efficient design.
method Support vector regression with kernel function and heuristic algorithm.
result Co_SVR outperforms other multi-fidelity surrogate models in prediction accuracy.
Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete …
A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.
problem Optimizing SVR parameters for reliable regression performance on small sample sizes.
method Golden Sine Algorithm (GSA) for parameter tuning of SVR.
result The GSA-based SVR outperforms eleven other meta-heuristics in terms of accuracy and computing time.
Paper proposes a hybrid model for VaR forecasting using SVR, GARCH, and KDE.
problem Inaccurate VaR estimates due to time-varying volatility and distributional characteristics.
method SVR-GARCH-KDE hybrid model combining nonlinear and nonparametric approaches.
result The SVR-GARCH-KDE hybrid outperforms benchmark models in VaR forecasting, especially for longer horizons.
We consider the stochastic composition optimization problem proposed in \cite{wang2017stochastic}, which has applications ranging from estimation to statistical and machine learning. We propose the first ADMM-based algorithm named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly convex and Lipsc…
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all sevent…
DCA algorithm applied to SVR with RBF kernel for nonconvex optimization.
problem Nonconvex optimization of SVR with Gaussian RBF kernel.
method DC algorithm with analytical DC decomposition of SVR objective.
result Convergence properties of DCA on RBF-SVR can be assessed through Cαρ. New model improves volatility forecasting by reducing overestimation and underestimation.
problem SVR-GARCH model overestimates or underestimates volatility, hindering peak or trough behaviors.
method Proposes blending ARCH and augmented blending-ARCH models to improve volatility forecasting.
result Empirical results show improved volatility forecasting ability.
Paper tackles multi-label learning by improving SVR for positive semidefinite metrics.
problem Learning positive semidefinite metrics for multi-label and label distribution learning.
method Proposes two methods to overcome SVR's limitation in learning positive semidefinite metrics.
result Demonstrates new methods achieve favorable performance in multi-label and label distribution learning.
Wavelet and LSTM models improve stock price forecasting.
problem Predicting non-stationary stock prices.
method Wavelet Transform for denoising, SVR and LSTM for prediction.
result Improved accuracy in stock price predictions.
Algorithm optimizes ε-SVR with MAPE loss and sample-dependent constraints.
problem Optimizing ε-SVR with MAPE loss and sample-dependent constraints.
method Sequential Minimal Optimization (SMO) for ε-SVR with MAPE loss and sample-dependent box constraints.
result Algorithm achieves lowest median runtime on every tested configuration.
Improved robustness in kernel-based regression via novel loss function and IRLS.
problem Noise sensitivity in kernel-based regression methods.
method Proposed ℓs-loss function and iteratively reweighted least squares (IRLS) optimization. result Improved noise robustness in kernel-based regression methods.
A new method connects GLM and MLE for neuroimaging analysis.
problem Limited mathematical elegance and interpretation of MLE for neuroimaging.
method Derives a refined statistical test using SVR-iGLM and RFT.
result MLE and GLM parameter estimations are significantly related to functional tasks.
Paper introduces a new SVR model using a combined reward and penalty loss function.
problem Regression problem, particularly handling data points outside and inside ε-tube.
method Combined reward cum penalty loss function to penalize and reward data points.
result Experimental results support the model's properties and effectiveness.
Study proposes a hybrid method for medium-term load forecasting.
problem Accurate medium-term load forecasting for power system operation and planning.
method Support Vector Regression (SVR) combined with Symbiotic Organism Search Optimization (SOSO) for parameter optimization and feature selection.
result The proposed method outperformed previous methods in the EUNITE competition dataset.
The paper improves SVR with linear constraints for better model properties.
problem Improving Support Vector Regression with linear constraints.
method Generalized SMO algorithm for solving optimization with linear constraints.
result The proposed method shows better practical performance on various datasets.
Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature s…
This paper uses SVM to predict stock market trends from financial news.
problem Predicting stock market trends using text mining and sentiment analysis.
method Text mining, sentiment analysis, support vector machine (SVM), parameter optimization.
result SVM models show significant influence of news on stock market, with parameter G having the main effect.
This paper improves stock price forecasting using grey correlation analysis and feature-weighted SVR.
problem Improving accuracy of stock price forecasting.
method Divided factors affecting stock price movement into behavioral and technical. Used grey correlation analysis to measure relationships and transform into characteristic weights. Applied feature-weighted SVR.
result Significantly improved forecast accuracy compared to unmodified data.
Statistical mechanics reveals phase transitions in ε-SVR error.
problem Understanding task precision in neural representations with variability.
method Statistical mechanics applied to ε-SVR. result Double-descent phenomenon in generalization error due to ε. New algorithms improve distributed optimization under specific conditions.
problem Distributed optimization problems with high communication costs.
method SVRS and AccSVRS algorithms combining gradient sliding and variance reduction.
result Achieved better communication complexity in distributed optimization.
A framework for transformer attention layers derived from SVR.
problem Developing principled attention mechanisms for transformers.
method Mapping self-attention to SVR, deriving new attention types.
result Improved transformer performance and efficiency.
A fusion approach combines audio and video features for emotion recognition.
problem Continuous emotion recognition using both visual and auditory modalities.
method Pre-trained CNN features from video frames and minimalistic auditory descriptors. Fusion at feature or prediction level. SVR for prediction.
result Improves CCCs of 0.749 and 0.565 for arousal and valence respectively.
In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface (IVS). The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow efficient online adaptive …
Model selection is a crucial issue in machine-learning and a wide variety of penalisation methods (with possibly data dependent complexity penalties) have recently been introduced for this purpose. However their empirical performance is generally not well documented in the literature. It is the goal of this paper to in…
A new network-based method for high-level data classification without normalization.
problem Evaluation and classification of data with hidden attribute interactions.
method A new network-building methodology based on attribute-attribute interactions, avoiding normalization.
result Improvement in accuracy of high-level classification using betweenness centrality.
The objective of this study is to investigate the efficient determination of C and γ for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between C and kernels and demonstrates that the deviation of geometric distance of ne…
SVM methods improve tack coat classification in French pavements.
problem Classifying thin tack coat layers in GPR data.
method Inverse Machine Learning approach applied to SVM/SVR methods.
result SVM/SVR methods accurately classify tack coat emulsion proportions.
CNN model predicts fluvial floods quickly and accurately.
problem Real-time flood prediction is computationally demanding.
method Deep Convolutional Neural Network (CNN) trained on 2D hydraulic model outputs.
result CNN model outperforms SVR in predicting flood inundation.
Music FaderNets learns high-level musical qualities from low-level attributes.
problem Learning high-level musical qualities from limited data and subjective labels.
method Model low-level attributes through feature disentanglement and latent regularization; infer high-level features from low-level representations using GM-VAEs.
result Model successfully learns intrinsic relationships between high-level features and low-level attributes with minimal labeled data.
A new network-based high-level data classification method using betweenness centrality.
problem Traditional data classification techniques focus on physical features, while high-level classification considers semantic meaning.
method Proposes a network-based high-level classification technique using betweenness centrality.
result Competent classification performance in nine real datasets compared to traditional models.
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and p…
New method builds complex networks from attribute interactions without normalization.
problem Improving high-level classification algorithms by capturing hidden attribute interactions.
method Proposes a new complex network building methodology based on attribute-attribute interactions, avoiding normalization.
result Demonstrates improved performance in high-level classification techniques.
Predicts routing congestion in FPGA designs using machine learning.
problem Routing congestion estimation is absent or inaccurate in HLS design methods.
method Machine learning to predict routing congestion in HLS.
result Accurately estimates routing congestion with errors of 6.71% and 10.05%.
Hybrid models are reinterpreted as Neuro-Symbolic AI designs to quantify uncertainty and variability.
problem Limited semantic interface for comparing hybrid models across domains.
method Reinterpret hybrid models as Neuro-Symbolic AI, translating them into explicit inference function and logic-belief decomposition.
result Metrics SVR and BD quantify uncertainty and variability in hybrid models.
The paper presents a new framework for complex Support Vector Regression as well as Support Vector Machines for quaternary classification. The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued data and considers two cases: a) the complex data are split into their …
A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. However, analytical estimates can be obtained only for particular combinations of analytical models of signal and noise, thus precluding its straightforward extension to deal with other arbitrary noise sour…
This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. Thes…
Hierarchical decoupling improves sample efficiency for complex robots.
problem Learning long-range behaviors on complex robots.
method Two-part policy: low-level imitation and high-level transfer, with KL regularization.
result Hierarchical transfer significantly improves zero-shot high-level transfer and stabilizes learning.
We study the stability properties of nonlinear multi-task regression in reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a. multi-task kernels, are appropriate for learning prob- lems with nonscalar outputs like multi-task learning and structured out- put prediction. We show that multi-task ke…