Squares-support Vector Machine Research Articles

A novel and intelligent chemometric-electrochemical-enzymatic biosensing procedure and mimicking a clinical condition environment to trick the red blood cells for counting them under physiological conditions: A new connection among chemometry, electrochemistry and hematology

Here, a novel electrochemical biosensing procedure has been developed for determination of the number of red blood cells (RBCs) under physiochemical conditions based on chemometric modeling of hydrodynamic differential pulse voltammetric (HDPV), and amperometric data as responses of a modified edge plane pyrolytic graphite electrode (EPPGE). In order to obtain a good sensitivity from the EPPGE, its surface was modified with a thin layer of multiwalled carbon nanotubes-ionic liquid (MWCNTs-IL). Catalase (CAT) was immobilized onto the surface of MWCNTs-IL/EPPGE with help of nafion. The response of the biosensor was based on electrochemical reduction of oxygen of the blood samples which was enhanced by a trick based on addition of hydrogen peroxide (H2O2) to blood samples which can be reduced by the CAT to produce extra oxygen. Prior to experiments, the solution in electrochemical cell was bubbled with pure N2 to purge the oxygen in the solution, but in order to increase the selectivity of the biosensor towards detection of the oxygen obtained from the red blood cells, voltammetric responses of the biosensor were modeled by multivariate chemometric calibration methods with the help of radial basis function-partial least squares (RBF-PLS), least squares-support vector machines (LS-SVM), recursive weighted partial least squares (rPLS), ant colony optimization-mathematical pre-processing selection by genetic algorithm-sample selection through a distance-based procedure-partial least squares-1 (ACO-GA-SS-PLS1), and radial basis function-artificial neural networks (RBF-ANN) to select the best method for determination of the number of the RBCs. The results confirmed the amperometric methods modeled by RBF-ANN showed the best performance for supporting the biosensor in determination of the number of the RBCs with a performance which had an excellent compatibility with the results of a hemocytometer. The results of this study as the newest application of chemometric-electrochemical methods can make a strong connection among electrochemists, chemometricians and hematologists to expand their collaborations on determination of blood factors.

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R2, MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction.

Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate families, with an accuracy rate exceeding 97%, except for the Naïve Bayes model. Regarding computational time, the Random Forest model requires significantly more time for training than other models. Regarding memory usage, the Least Squares Support Vector Machine with a polynomial kernel, and Regularized Logistic Regression consume more memory than other models. These machine learning models exhibit strong concordance with NCBI's classifications when predicting families using the test dataset, effectively categorizing species into the Amaryllidaceae and Liliaceae families.

Improved fuzzy sparse multi-class least squares support vector machine

The classification problem is a key area of research in machine learning. The Least Squares Support Vector Machine (LSSVM) is an important classifier that is commonly used to solve classification problems. Its widespread use stems from its replacement of the inequality constraint in the Support Vector Machine (SVM) with the equality constraint, which transforms the convex quadratic programming (QP) problem of SVM into the solution of linear equations. However, when dealing with multi-class classification problems, LSSVM faces the challenges of lack of sparsity and sample noises, which can negatively impact its performance. Based on the modeling characteristics and data distribution of the multi-class LSSVM model, this paper proposes two improvements and establishes an improved fuzzy sparse multi-class least squares support vector machine (IF-S-M-LSSVM). The first improvement adopts a non-iterative sparse algorithm, which can delete training sample points to different degrees by adjusting the sparse ratio. The second improvement addresses the impact of sample noise on determining the optimal hyperplane by adding a fuzzy membership degree based on sample density. The advantages of the new model, in terms of training speed and classification accuracy, are verified through UCI machine learning standard data set experiments. Finally, the statistical significance of the IF-S-M-LSSVM model is tested using the Friedman and Bonferroni-Dunn tests.

Non-destructive detection of protein content in mulberry leaves by using hyperspectral imaging.

Protein content is one of the most important indicators for assessing the quality of mulberry leaves. This work is carried out for the rapid and non-destructive detection of protein content of mulberry leaves using hyperspectral imaging (HSI) (Specim FX10 and FX17, Spectral Imaging Ltd., Oulu, Finland). The spectral range of the HSI acquisition system and data processing methods (pretreatment, feature extraction, and modeling) is compared. Hyperspectral images of three spectral ranges in 400-1,000 nm (Spectral Range I), 900-1,700 nm (Spectral Range II), and 400-1,700 nm (Spectral Range III) were considered. With standard normal variate (SNV), Savitzky-Golay first-order derivation, and multiplicative scatter correction used to preprocess the spectral data, and successive projections algorithm (SPA), competitive adaptive reweighted sampling, and random frog used to extract the characteristic wavelengths, regression models are constructed by using partial least square and least squares-support vector machine (LS-SVM). The protein content distribution of mulberry leaves is visualized based on the best model. The results show that the best results are obtained with the application of the model constructed by combining SNV with SPA and LS-SVM, showing an R 2 of up to 0.93, an RMSE of just 0.71 g/100 g, and an RPD of up to 3.83 based on the HSI acquisition system of 900-1700 nm. The protein content distribution map of mulberry leaves shows that the protein of healthy mulberry leaves distributes evenly among the mesophyll, with less protein content in the vein of the leaves. The above results show that rapid, non-destructive, and high-precision detection of protein content of mulberry leaves can be achieved by applying the SWIR HSI acquisition system combined with the SNV-SPA-LS-SVM algorithm.

Research on robust inversion model of soil moisture content based on GF-1 satellite remote sensing

This study aims to improve the accuracy and applicability of traditional linear regression and machine learning algorithms for monitoring soil moisture content by satellite remote sensing. Shahaoqu Experimental Station within Jiefangzha Irrigation District of Hetao Irrigation Area is selected as the study area. The dataset includes GF-1 satellite remote sensing images and measured soil moisture content. The study employs the best subset selection to determine the combination of sensitive variables for soil moisture content at different soil depths. Additionally, robust regression theory is introduced to traditional linear regression and machine learning algorithms to construct soil moisture content inversion models at different soil depths. The results show that all three improved robust algorithms effectively reduce the influence of outliers, improve the accuracy and stability of these inversion models. The improved robust nonlinear algorithm outperforms the improved robust linear algorithm. Compared with the unprocessed algorithm, at the soil depth of 0–20 cm, Robust Extreme Learning Machine (RELM) algorithm is the most optimal, with the validation set's R2adj increasing from 0.555 to 0.696 (approximately 25.33%) and the RMSE decreasing from 0.025 to 0.018 (approximately 26.54%). Robust Least Squares Support Vector Machine (RLSSVM) algorithm is the second best, with the validation set's R2adj increasing from 0.502 to 0.641 (approximately 27.75%) and the RMSE decreasing from 0.026 to 0.020 (approximately 24.59%). Robust Linear Regression (LMS-RLS) algorithm is relatively the worst, with the validation set's R2adj increasing from 0.491 to 0.659 (approximately 34.05%) and the RMSE decreasing from 0.026 to 0.019 (approximately 27.21%). The sensitivity of the five soil depths to soil moisture content follows the order of 0–20 cm, 20–40 cm, 0–60 cm, 0–40 cm, and 40–60 cm. The improved robust regression algorithms can improve the accuracy and applicability of soil moisture content estimation and provide a reference for monitoring soil moisture content using GF-1 satellite.

Monitoring and detecting coal miners' fatigue status using MPA-LSSVM in the vision of smart mine

With booming computer technology and diverse computer-based smart applications, intelligent monitoring and detection of the fatigue state of coal miners (miners) has attracted extensive attention from enterprises. In order to accurately and fast knowledge the fatigue status of Miners and reduce production accidents. The article proposes a Fatigue Monitoring and Detection (FMD) model based on a fusion Machine Learning (ML) approach: Marine Predators Algorithm (MPA)-optimized Least Squares Support Vector Machine (LSSVM). Firstly, the physiological information of electroencephalogram (ECG) of coal miners before and after manual handling operations was collected using the MP160 recorder (multiconductance physiological 160) produced by BIOPAC, USA. Using the paired-samples t-test method, the characteristic indicators reflecting miners' fatigue were extracted from the ECG. Secondly, Principal Component Analysis (PCA) was used to optimize the selected feature indicators and establish the depth fatigue feature parameter set to characterize the fatigue level of miners. Finally, the proposed MPA-LSSVM-based FMD model is applied to recognize Miners' fatigue levels. The results show that the selected indexes can effectively reflect the fatigue status of Miners. The proposed MPA-LSSVM-based FMD model has higher recognition accuracy than SVM and LSSVM models (13.99% and 18.68% higher, respectively) and better robustness. Therefore, the proposed MPA-LSSVM-based FMD model can accurately and effectively identify the fatigue status of Miners.

Klasifikasi Nasabah Potensial menggunakan Algoritma Ensemble Least Square Support Vector Machine dengan AdaBoost

In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.

Crosstalk tolerance analysis of coupled-line structures using least square-support vector machine technique

In this paper, crosstalk sensitivity analysis of a microwave coupled-line structure due to the fabrication imperfections is investigated using Least Square-Support Vector Machine (LS-SVM) method. Since LS-SVM uses a set of linear equations instead of a convex quadratic programming problem, the computational cost is extremely reduced compared to that of the well-known Monte Carlo (MC) analysis or even Support Vector Machine (SVM) without decreasing the accuracy. Using this method, the geometrical parameters of the coupled-line are assumed to be randomly distributed using the Latin Hypercube function and the variation range of each parameter is set to ± 50% around its central value. The frequency response of the coupled-line is estimated and compared with those of the measured and simulation ones for a few well-known practical case studies. The results show that the LS-SVM procedure quickly predicts the worst-case crosstalk expectation values and accurately anticipates the probability of obtaining various outcomes of the coupled-line for the specified parameter variation over a wide frequency range.

A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan–Tibet Highway, China

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering.

Comparison of SVMR and PLSR for ATR-IR data treatment: Application to AQC of mAbs in clinical solutions

Attenuated Total Reflectance Infrared spectroscopy (ATR-IR) enables rapid, preparation-free and cost-effective analysis of many clinically relevant samples. For instance, it can substitute traditional techniques like high performance liquid chromatography (HPLC) for Analytical Quality Control (AQC) of therapeutic solutions of anticancer drugs like monoclonal antibodies (mAbs). More reliable quantitative interpretation of the ATR-IR spectra is usually achieved with multivariate data mining protocols. In the present study, the analytical performance of ATR-IR was evaluated for four clinical solutions of mAbs: Bevacizumab (Avastin®), Trastuzumab (Ontruzant®), Cetuximab (Erbitux®) and Rituximab (Truxima®). As expected, the ATR-IR spectra were a combination of the bands of the antibody itself and those of the excipients. For quantitative analysis of the solutions, two types of regression methods have been compared namely, Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR). While both approaches were relevant, the latter gave better performances in terms of accuracy, linearity and the percent relative error that was below 15%. The results are thus promising for future applications of ATR-IR Spectroscopy coupled to the SVMR algorithm as a clinical tool for AQC of mAbs.

Combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction

Displacement is a crucial indicator reflecting the safety operational state of concrete arch dams. Consequently, the application of displacement monitoring models for arch dam structural health assessment has become widely prevalent. However, conventional singular displacement monitoring methods exhibit limited information extraction capabilities and lack precision, resulting in underexplored residual sequences from the monitoring model and unexcavated valuable information. To address this, this paper integrated cluster analysis with Long Short-Term Memory (LSTM), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Least Squares Support Vector Machine (LSSVM), and Particle Swarm Optimization (PSO) methods to establish a combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction. Firstly, a model combining cluster analysis k-means++ with LSTM (k-LSTM), was proposed for displacement prediction of arch dams. Secondly, CEEMDAN was employed to adaptively decompose the residual sequences, where Pearson correlation coefficient (PCC) was introduced to filter and reconstruct intrinsic mode components. Subsequently, PSO-LSSVM was utilized to predict the reconstructed sequences and obtain residual correction values. Finally, based on the summation of residual correction values and k-LSTM model predictions, the arch dam displacement combination prediction model was constructed. Engineering case demonstrates the efficiency of this model in arch dam displacement prediction. By effectively mining and utilizing valuable information from residual sequences, this combined model exhibits superior generalization capabilities over traditional singular models, thereby providing technical support and novel research perspectives for concrete arch dam health monitoring and operational management.

Identification effect of least square fitting method in archives management

Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification. A new wavelet function is used to improve the classifier. At the same time, the cross-validation method is used to optimize the kernel parameters. Finally, the fuzzy theory and LS-SVM are combined to obtain Fuzzy Least Squares Support Vector Machines (FLS-SVM). This FLS-SVM classifier can use the distance between the data points and the classification hyperplane to classify the data in the non-separable region. The performance of FLS-SVM is verified by simulation experiments. The experimental results show that the classification accuracy of FLS-SVM classifier in archive data sets is 98.7%, and the loss rate is only 0.26%. When the wavelet function is used as the kernel function, the average accuracy of the classifier reaches 98.38%. Experiments show that the proposed method has good classification performance. It verifies the feasibility and effectiveness of the least squares fitting method in file management identification and classification.

A novel prediction model construction and result interpretation method for slope deformation of deep excavated expansive soil canals

Several giant water diversion projects go through large expansive soil areas in China. It is challenging to ensure the slope stability of the deep excavated expansive soil canal segments due to its undesirable geologic, and physical and mechanical properties. This study develops a novel deformation prediction method for deep excavated expansive soil canal slopes based on the eXtreme Gradient Boosting (XGBoost) algorithm, and provides a result interpretation using SHAP (SHapley Additive exPlanations). Firstly, the main factors influencing the slope deformation of deep excavated expansive soil canal segments are discussed. Subsequently, the integrated XGBoost-SHAP method is proposed for the prediction model construction of slope deformation and its result interpretation, the related the methodologies including the variational mode decomposition (VMD), XGBoost, and SHAP are also presented. The ten-fold cross-validation is employed to find the hyperparameter combination for the deformation prediction models, and the model performances are compared with common validation measurements. The effectiveness of the explanatory deformation prediction method is verified through one typical deep excavated expansive canal segment of the mid-route of the South-to-North Water Diversion Project in China. The case study shows that the VMD algorithm can well decompose the trend, periodic and fluctuating displacements. Compared with random forest and least squares-support vector machines, the XGBoost-algorithm based model achieves the better prediction performance. SHAP provides prediction result interpretations at both global and local levels. Time dependent effect and groundwater have a significant impact on the slope deformation of deep excavated expansive soil canal segments. The integrated SHAP-XGBoost method can provide a reference for predicting slope deformation of the similar projects. It can also quantify the contribution and mechanism of influencing factors on slope deformation at both global and local levels, which is conducive to implement reinforcement measures.

Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction

In order to provide an accurate and reliable effective state-of-health (SOH) estimation, a novel hybrid data-driven estimation method by failure feature extraction is proposed. Firstly, influencing factors which reflect the failure of lithium-ion power batteries are studied, and three failure features of lithium-ion power batteries used as inputs of the estimation model are extracted by fuzzy grey relational analysis (FGRA) method. Then, the improved Least Squares Support Vector Machine (LSSVM) model is employed to estimate the SOH under different ambient temperature conditions. The results show that CC charging time, CV charging capacity and CV charging average temperature are determined as the failure features of the SOH estimation model, whose correlation degree to the battery capacity are 0.8774, 0.8104 and 0.8771, respectively. Compared with SVM, the improved LSSVM model has higher SOH estimation accuracy for the lithium-ion power battery under different ambient temperature conditions. In addition, the SOH estimation curves basically matches the actual curves, where the SOH estimation errors are less than 0.02. Moreover, the mean square error accuracy of the prediction results is at the level of 0.00001, and the determination coefficient is between 0.92 and 0.997. This work provides reference for enhancing the SOH estimation performance and safety of lithium-ion power batteries.

基于高光谱成像技术的欧李果成熟度判别

In order to realize the rapid and accurate identification of different maturity of Cerasus humilis fruit, this study explored the nondestructive testing method of Cerasus Humilis fruit maturity based on hyperspectral imaging technology. The hyperspectral data of 320 samples of Cerasus humilis fruit were collected by using a hyperspectral imaging system in the range of 895~1700 nm. By comparing the prediction accuracy of the partial least squares (PLS) model established by four preprocessing methods, the competitive adaptive reweighted algorithm (CARS), successive projection algorithm (SPA), and random frog (RF) were used to extract characteristic wavelengths, and partial least squares-discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) discriminant models were established. The results showed that the SPA-LS-SVM model had the highest discrimination accuracy for the four types of maturity samples, and the discrimination accuracy of the correction set and prediction set were 85.00% and 87.50%, respectively. This study provides a theoretical reference for the rapid and nondestructive testing of the maturity of Cerasus Humilis fruit by hyperspectral imaging technology.

Retracted: Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM).

[This retracts the article DOI: 10.1155/2022/4792988.].

Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution

In this study, hybrid machine learning nonlinear models were developed to predict the viscosity of DESs by combining the group contribution (GC) concept with the multilayer perceptron, a well-known feedforward artificial neural network, and the Least Squares Support Vector Machine (LSSVM) algorithm. Deep Eutectic Solvents (DESs) have come to the forefront in recent years as appealing substitutes for conventional solvents. It is imperative to have a thorough grasp of the essential properties of DESs to expand the employment of these compounds in new procedures. Most frequently, one of the crucial physical properties of a DES that must be precisely determined is its viscosity. To develop the models, a dataset of 2533 viscosity data points for 305 DESs at various temperatures (from 277.15 to 373.15 K) was gathered to build the models. By using temperature, molar ratios, and functional groups as inputs, the results indicate that the suggested models can determine the viscosity of DESs with high accuracy. The models yield average absolute relative deviations below 10% and squared correlation coefficients higher than 0.98.

Experimental investigation and machine learning modeling of heat transfer characteristics for water based nanofluids containing magnetic Fe3O4 nanoparticles

The convective heat transfer coefficients of Fe3O4 magnetic nanofluids in both laminar and turbulent flow states inside a pipe were determined as base values and adequately simulated using machine learning techniques. Three selected machine learning methods for the simulations were Multiple Linear Regression Analysis (MLR), Radial Basis Function-Backpropagation (RBF-BP), and Least Squares-Support Vector Machines (LS-SVM). Initially, MLR was employed to fit the polynomial equation, followed by the selection of the best RBF-BP model with 6 hidden layer neurons using the grid search cross-validation method. For the LS-SVM model, a kernel function of 0.3 and a regularization parameter of 100 were used. By conducting a detailed comparison of the numerical patterns of the accuracy evaluation parameters, the RBF-BP and LS-SVM models were evaluated, with the LS-SVM model demonstrating superiority. The main parameters in the Reynolds number-mass fraction simulation were the mean square error (MSE) and regression coefficient (R2), which obtained specific values of MSE= 0.34 and R2 = 0.99994 under laminar flow conditions. Similarly, in the Reynolds number-magnetic field strength simplification, the best values for laminar flow conditions were MSE= 3.85 and R2 = 0.99993. The validity and accuracy of the model predictions were further demonstrated through visual comparisons of simulated and experimental values using Three-Dimensional smoothed surface plots. The groundbreaking discoveries hold the potential to catalyze advancements and foster significant progress in the fields of machine learning and nanotechnology. As a valuable resource, these results play a crucial role in propelling further research and development efforts, thus making noteworthy contributions to the continuous growth and innovation in these cutting-edge disciplines.

A self-error corrector integrating K-means clustering with Markov model for marine craft maneuvering prediction with experimental verification

A novel approach for maneuvering prediction of marine crafts in data-limited situations is presented in this study. The approach is a hybrid strategy that combines least square-support vector machines (LS-SVM) and a self-error corrector that integrates K-means clustering with Markov model. The strategy allows testing errors caused by LS-SVM to be compensated effectively by the dedicated self-error corrector. To classify the inputs of the Markov model-based error corrector rapidly and properly, the K-means clustering algorithm is resorted to classifying the training errors obtained from LS-SVM. Assuming that maneuvering prediction for marine crafts with a limited number of simulation or experiment data, a comparative analysis of the prediction performance between the proposed strategy and the conventional method based on LS-SVM is performed. The results of numerical simulations and experiments demonstrate the superiority of the proposed strategy, which reduces the root mean square error of surge speed by up to 86.2% in simulation tests and 54.5% in experiments.