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We propose a data mining approach to predict the wine's quality level in order to improve the quality of products for wine enterprises in this paper. A large dataset is considered and three regression techniques were applied. Through the comparison, we get the conclusion that the model established by neural network is more accurate and it can improve the quality of wine's production.
In this study, a hybrid model using multivariate adaptive regression splines (MARS) and SVR is proposed for sales forecasting of information technology (IT) products. Support vector regression (SVR) has become a promising alternative for forecasting due to its generalization capability in obtaining a unique solution. However, one of the key problems is that SVR can not identify which forecasting variables...
Locally weighted learning (LWL), which is an effectual and flexible method for prediction problems, is widely used in many regression scenarios. The training data samples, referring to the history experience knowledge base, are required to help do regression by new queries. However, sometimes, the knowledge base tends to be helpless due to the lake of information, such as inadequate training data...
A new online prediction model based on Adaptive Recursive Least Squares Support Vector Machine (ARLSSVM) is presented in this paper, and applied to predict silicon-manganese alloy composition in a 30MVA submerged arc furnace smelting process. By using Recursive Least Squares Support Vector Machine (RLSSVM) regression algorithm, it avoids the difficulty of solving high-dimensional inverse matrix and...
In this paper, we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex. On this basis, the author combines...
This proposal presents the integration of interpolating global cubic splines into a general function regression framework to approximate curved functions or more dimensional curves based on noisy observations. We rearrange the iterative process of spline parameter calculation and obtain a practical linear matrix formulation. Then we employ Bayesian techniques to estimate spline model parameters and...
This paper represents a fusion model of functional link artificial neural network (FLANN) based on Kernel Regression (KR) for modeling and prediction of exchange rate time series. To predict the exchange rate, we process the exchange rate datasets with KR to smooth the noise. And then the smoothed datasets are nonlinearly expanded using the sine and cosine expansions before inputting to the FLANN...
The basic principle of combination forecasting is to give the proper weight combination into a single composite model from the results of each single forecasting model. Therefore, in the process of combination, each single model's advantages strengthened, and disadvantages weaken. Combination forecasting model has higher accuracy and reliability by integrating useful information of each single model...
The performance of a model, which is trained with offline data, is highly relied on the conditions in which the system is working. When the working conditions change, the prediction accuracy of the model will be reduced significantly. To solve this problem, we propose an adaptive SVR modeling method based on vector-field-smoothed (VFS) algorithm. This method can adapt the model quickly to new working...
Wireless sensor networks (WSNs) are data centric networks to which data aggregation is a central mechanism. Nodes in such networks are known to be of low complexity and highly constrained in energy. This requires novel distributed algorithms to data aggregation, where accuracy, complexity and energy need to be optimized in the aggregation of the raw data as well as the communication process of the...
Yield is a very important criterion to measure the semiconductor wafer fabrication facilities (FABs) productivity. The finished products will be check by Wafer Acceptance Test (WAT) and Circuit Probe (CP) to classified into ferior goods or inferior goods. This research applied the data from WAT and CP for the selection of the most important measuring parameters to improve the yield. Three methods,...
This article describes a novel framework for combining time series forecasts. It uses neural network regression models to estimate, at a given point in time, the linear weights (relevancies) of the available experts (forecasters) at that time. With those weights, the experts can be linearly combined to produce a single, potentially more accurate, forecast. This new weight generation framework was...
This paper proposes a novel admission and replacement technique for web caching, which utilizes the multinomial logistic regression (MLR) as classifier. The MLR model is trained for classifying the web cache's object worthiness. The parameter object worthiness is a polytomous (discrete) variable which depends on the traffic and the object properties. Using worthiness as a key, an adaptive caching...
A new optimal strategy based on symbiotic modelling is proposed. The system combines Linear Regression Model (LR), Non-Linear Iterative Partial Adaptive Least Square Model (NIPALS), Neural Network Model with double loop procedures (NNDLP), Adaptive Numeric Modelling (Neural-Fuzzy modeling NF) and metallurgical knowledge in order to provide effective modelling solutions and achieve an optimal prediction...
Prognosis of traffic flow is a basic part of intelligent transportation research. Due to the extremely complexity of vehicular traffic, efficient models should be constructed to do accurate simulation and prediction of real traffic, such as locally kernel models. However, locally kernel regression fails when the traffic data points are sparse, and the data distribution should be considered seriously...
Multi-level classification of web objects in caching is relatively an unexplored area. This paper proposes a novel caching scheme which utilizes a multi-level class information. A MLR (Multinomial Logistics Regression) based classifier is constructed using the information from web logs. Simulation results confirm that the model has good prediction capability and suggest that the proposed approach...
Universal Mobile Telecommunication System (UMTS) is a third generation mobile communication systems that supports wireless wideband multimedia applications. The objective of this paper is to present a new model for non-intrusive prediction of H.264 encoded video quality over UMTS networks and to illustrate their application to video quality monitoring and adaptation in mobile wireless streaming services...
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical...
In the context of linear regression, the least absolute shrinkage and selection operator (LASSO) is probably the most popular supervised-learning technique proposed to recover sparse signals from high-dimensional measurements. Prior literature has mainly concerned itself with independent, identically distributed noise with moderate variance. In many real applications, however, the measurement errors...
Econometric models can be very useful for estimating the marginal impacts of changes in policy. However, their broader application as a tool for micro-simulation analysis poses a number of challenges and limitations. This paper uses the context of modeling taxpayer compliance burden for small businesses to explore some extentions to standard econometric simulation techniques that provide more robust...
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