Temat: support vector regression

Skocz do pozycji: 1.

Tytuł:: Model of delays in public transport in Kraków
Model opóźnień w komunikacje miejskiej w Krakowie
Autorzy:: Smykała, Szymon
Opis:: The master's thesis titled "Model of delays in public transport in Kraków" aimed to: collect delays data, analyze these delays, and model the average delays during morning and afternoon peak hours on weekdays. In the first stage, based on data from the TTSS system (Mobile Passenger Information in Kraków), delays were calculated for each vehicle departure from a stop, and at the same time, delay data was archived in a database. In the next stage, the data was analyzed: the number of departures from stops, transits were presented, extremes were identified, and an attempt was made to fit a distribution. In the final stage, models were created to estimate the average delay during morning peak hours (7:00-9:00) and afternoon peak hours (14:00 – 18:00). Algorithms such as linear regression, Support Vector Regression, and random forest were utilized in the study.
Praca magisterska zatytułowana "Model opóźnień komunikacji miejskiej w Krakowie" miała na celu: zebranie danych o opóźnieniach, analizę opóźnień oraz zamodelowanie średnich opóźnień w porannych i popołudniowych godzinach szczytu w dni robocze. W pierwszym etapie na podstawie danych z systemu TTSS (Mobilnej Informacji Pasażerskiej w Krakowie) zostały obliczone opóźnienia na każdym odjeździe pojazdu z przystanku, jednocześnie dane o opóźnieniach były archiwizowane w bazie danych. W kolejnym etapie dane zostały przeanalizowane: zaprezentowano liczbę odjazdów z przystanków, przejazdów, zidentyfikowano ekstrema, dokonano próby dopasowania rozkładu. Na ostatnim etapie stworzono modele estymujące średnie opóźnienie w porannych godzinach szczytu (7:00-9:00) i popołudniowych godzinach szczytu (14:00 – 18:00). W badaniach wykorzystano algorytmy takie jak regresja liniowa, Support Vector Regression oraz las losowy.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 2.

Tytuł:: Intermittent demand forecasting using data mining techniques
Autorzy:: Kaya, Gamze Ogcu
Turkyilmaz, Ali
Tematy:: ANN
support vector regression
Intermittent Demand Forecasting
regresja wektora nośnego; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/117926.pdf Link otwiera się w nowym oknie
Opis:: Intermittent demand occurs randomly with changing values and a lot of periods having zero demand. Ad hoc intermittent demand forecasting techniques have been developed which take special intermittent demand characteristics into account. Besides traditional techniques and specialized methods, data mining offers a better alternative for intermittent demand forecasting since data mining methods are powerful techniques. This study contributes to the current literature by showing the benefit of using data mining methods for intermittent demand forecasting purpose by comprising mostly used data mining methods.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A fuzzy KNN-based model for significant wave height prediction in large lakes
Autorzy:: Nikoo, M.R.
Kerachian, R.
Alizadeh, M.R.
Tematy:: wave height
prediction
fuzzy set theory
lake
Bayesian network
support vector regression; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Oceanologii PAN
Powiązania:: https://bibliotekanauki.pl/articles/48113.pdf Link otwiera się w nowym oknie
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Incrementally Solving Nonlinear Regression Tasks Using IBHM Algorithm
Autorzy:: Zawistowski, P.
Arabas, J.
Tematy:: black-box modeling
neural networks
nonlinear approximation
nonlinear regression
support vector regression
weighted correlation; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/308427.pdf Link otwiera się w nowym oknie
Opis:: This paper considers the black-box approximation problem where the goal is to create a regression model using only empirical data without incorporating knowledge about the character of nonlinearity of the approximated function. This paper reports on ongoing work on a nonlinear regression methodology called IBHM which builds a model being a combination of weighted nonlinear components. The construction process is iterative and is based on correlation analysis. Due to its iterative nature, the methodology does not require a priori assumptions about the final model structure which greatly simplifies its usage. Correlation based learning becomes ineffective when the dynamics of the approximated function is too high. In this paper we introduce weighted correlation coefficients into the learning process. These coefficients work as a kind of a local filter and help overcome the problem. Proof of concept experiments are discussed to show how the method solves approximation tasks. A brief discussion about complexity is also conducted.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting
Autorzy:: Hung, W. M.
Hong, W. C.
Tematy:: support vector regression (SVR)
continuous ant colony optimization algorithms (CACO)
exchange rates
financial forecasting; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/969706.pdf Link otwiera się w nowym oknie
Opis:: Traditional time series forecasting models, like ARIMA and regression models, can hardly capture nonlinear patterns. Support vector regression (SVR), a novel neural network technique, has been successfully used to solve nonlinear regression and time series problems. The SVR model applies the structural risk minimization principle to minimize the upper bound of the generalization error, instead of minimizing the training error, employed by most conventional neural network models. Thus, parameter determination for an SVR model is appropriate for achieving high forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms have been used in parameter selection, but these algorithms often suffer from the possibility of being trapped in local optimum. This study used an improved ant colony optimization algorithm in an SVR model, called SVRCACO, for selecting suitable parameters, with encouraging local search in areas where forecasting accuracy improvement continues to be made, then, autocatalytically converge to promising regions. Numerical examples of exchange rate forecasting from the existing literature are employed to assess the performance of the proposed model. Experimental results show that the proposed model outperforms other approaches from the literature.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Voice Conversion Based on Hybrid SVR and GMM
Autorzy:: Song, P.
Jin, Y.
Zhao, L.
Zou, C.
Tematy:: voice conversion
support vector regression
Gaussian mixture models
F0 prediction
speaker-specific information; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/177748.pdf Link otwiera się w nowym oknie
Opis:: A novel VC (voice conversion) method based on hybrid SVR (support vector regression) and GMM (Gaussian mixture model) is presented in the paper, the mapping abilities of SVR and GMM are exploited to map the spectral features of the source speaker to those of target ones. A new strategy of F0 transfor- mation is also presented, the F0s are modeled with spectral features in a joint GMM and predicted from the converted spectral features using the SVR method. Subjective and objective tests are carried out to evaluate the VC performance; experimental results show that the converted speech using the proposed method can obtain a better quality than that using the state-of-the-art GMM method. Meanwhile, a VC method based on non-parallel data is also proposed, the speaker-specific information is investigated us- ing the SVR method and preliminary subjective experiments demonstrate that the proposed method is feasible when a parallel corpus is not available.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression
Autorzy:: Wang, Siming
Zhao, Kaikai
Tematy:: distributed generation
distribution network fault location
fault type
Karrenbauer transform
agent prediction model
SVR
support vector regression; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/24202729.pdf Link otwiera się w nowym oknie
Opis:: As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Comparison of data mining techniques to predict and map the Atterberg limits in central plateau of Iran
Autorzy:: Amin, Peyman
Taghizadeh-Mehrjardi, Ruhollah
Akbarzadeh, Ali
Shirmardi, Mostafa
Tematy:: Atterberg limits, artificial bee colony, artificial neural networks, support vector machine, regression tree; Pokaż więcej
Wydawca:: Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Powiązania:: https://bibliotekanauki.pl/articles/762833.pdf Link otwiera się w nowym oknie
Opis:: The Atterberg limits display soil mechanical behavior and, therefore, can be so important for topics related to soil management. The aim of the research was to investigate the spatial variability of the Atterberg limits using three most common digital soil-mapping techniques, the pool of easy-to-obtain environmental variables and 85 soil samples in central Iran. The results showed that the maximum amount of liquid limit (LL) and plastic limit (PL) were obtained in the central, eastern and southeastern parts of the study area where the soil textural classes were loam and clay loam. The minimum amount of LL and PL were related to the northwestern parts of the study area, adjacent to the mountain regions, where the samples had high levels of sand content (>80%). The ranges of plasticity index (PI) in the study area were obtained between 0.01 to 4%. According to the leave-in-out cross-validation method, it should be highlighted the combination of artifiial bee colony algorithm (ABC) and artifiial neural network (ANN) techniques were the best model to predict the Atterberg limits in the study area, compared to the support vector machine and regression tree model. For instance, ABC-ANN could predict PI with RMSE, R2 and ME of 0.23, 0.91 and -0.03, respectively. Our fiding generally indicated that the proposed method can explain the most of variations of the Atterberg limits in the study area, and it could berecommended, therefore, as an indirect approach to assess soil mechanical properties in the arid regions, where the soil survey/sampling is difficult to undertake.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator
Autorzy:: Kumar, M.
Tiwari, N. K.
Ranjan, S.
Tematy:: volumetric oxygen transfer coefficient
multiple nonlinear regression
Gaussian process regression
support vector regression
współczynnik wnikania tlenu
regresja nieliniowa
proces gaussowski
regresja wektora wsparcia; Pokaż więcej
Wydawca:: Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Powiązania:: https://bibliotekanauki.pl/articles/368684.pdf Link otwiera się w nowym oknie
Opis:: Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data
Porównanie metod uczenia maszynowego do prognozowania spływu w zlewniach górskich na podstawie ograniczonych danych
Autorzy:: Adamowski, J.
Prasher, S. O.
Tematy:: Himalaje
prognozowanie spływu
regresja wektora wsparcia
sieci falkowe
uczenie maszynowe
Himalayas
machine learning
runoff forecasting
support vector regression
wavelet networks; Pokaż więcej
Wydawca:: Instytut Technologiczno-Przyrodniczy
Powiązania:: https://bibliotekanauki.pl/articles/292443.pdf Link otwiera się w nowym oknie
Opis:: Runoff forecasting in mountainous regions with processed based models is often difficult and inaccurate due to the complexity of the rainfall-runoff relationships and difficulties involved in obtaining the required data. Machine learning models offer an alternative for runoff forecasting in these regions. This paper explores and compares two machine learning methods, support vector regression (SVR) and wavelet networks (WN) for daily runoff forecasting in the mountainous Sianji watershed located in the Himalayan region of India. The models were based on runoff, antecedent precipitation index, rainfall, and day of the year data collected over the three year period from July 1, 2001 and June 30, 2004. It was found that both the methods provided accurate results, with the best WN model slightly outperforming the best SVR model in accuracy. Both the WN and SVR methods should be tested in other mountainous watershed with limited data to further assess their suitability in forecasting.
Prognozowanie spływu z obszarów górskich z użyciem programowanych modeli jest często trudne i niedokładne z powodu złożonych zależności między opadem a spływem i problemów związanych z pozyskaniem niezbędnych danych. Modele uczenia maszynowego stwarzają alternatywę dla prognozowania spływu z takich regionów. W pracy analizowano i porównano dwie metody uczenia maszynowego - metodę regresji wektorów nośnych (SVR) i sieci falkowych (WN) do dobowego prognozowania spływu w górskiej zlewni Sianji, usytuowanej w indyjskiej części Himalajów. Modele opracowano na podstawie danych o spływie, wskaźniku poprzednich opadów, opadzie i kolejnym dniu roku za trzyletni okres od 1 lipca 2001 r. do 30 czerwca 2004 r. Stwierdzono, że obie metody zapewniają dokładne wyniki, przy czym najlepszy model WN nieco przewyższa najlepszy model SVR pod względem dokładności. Obie metody powinny być testowane w innych zlewniach górskich o ograniczonej liczbie danych, aby lepiej ocenić ich przydatność do prognozowania.
Dostawca treści:: Biblioteka Nauki

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