The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recommendation systems apply statistical and knowledge discovery techniques to the problem of making product recommendations and they are achieving widespread success in E-Commerce these days. A successful recommendation system fulfils several purposes and the choice of the methodology significantly influences the quality of recommendations and other aspects including scalability. As the volume of...
The amount of download prediction or forecast is a statement about the way things will happen in the future, often but not always based on experience or knowledge. While there is much overlap between prediction and forecast, a prediction may be a statement that some outcome is expected, while a forecast may cover a range of possible outcomes. Although guaranteed information about the information is...
The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving extended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed...
Predicting future traffic congestion has the potential to decrease travel times by improving GPS navigation and enhancing traffic flows. This paper describes a methodology developed to predict future automated traffic recorder (ATR) readings with current and recent ones from local ATRs. Data was preprocessed by down sampling the simulated ATR signals. Additional training sets were created by resampling...
Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools that learn from past projects' data. In this study, we have built an effort estimation model for a multinational bank to predict the effort prior...
The automatic assessment of aesthetic values in consumer photographic images is an important issue for content management, organizing and retrieving images, and building digital image albums. This paper explores automatic aesthetic estimation in two different tasks: (1) to estimate fine-granularity aesthetic scores ranging from 0 to 100, a novel regression method, namely Diff-RankBoost, is proposed...
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...
We present an improved online learning algorithm for sparse kernel partial least squares, this algorithm improves current methods to kernel-based regression in two aspects. First, it operates online at each time step when it acquires a new input support vector, performs an update and drop out the old data to adapted process changes. Second, it effectively reduces the dimension of feature space and...
A data stream prediction algorithm using Linear Regression based on Exponential Smoothing method was proposed in this paper, namely Exponential Smoothing based Linear Regression Analysis (ES_LRA) data stream prediction algorithm. The ES_LRA algorithm only processes the current sliding window, which can improve the operation efficiency; In the meantime, it applied a Smoothing Coefficient(α) through...
On the pre-estimation of indices of wastewater at the infall of Waste Water Treatment Plant (WWTP) in the confluent flow dynamic system,Multivariate Linear Regression (MLR) is presented and applied effectively in general case. However, the Partial Regression Coefficients (PRC) identified from the data model is not suitable for the continuous time varying process occasionally. Therefore, an improved...
Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. In many real life problems, we would like to predict multiple related (nominal or numeric) target attributes simultaneously. Methods for learning rules that predict multiple targets at once already exist, but are unfortunately...
In this paper, we present a novel method for fast data-driven construction of regression trees from temporal datasets including continuous data streams. The proposed mean output prediction tree (MOPT) algorithm transforms continuous temporal data into two statistical moments according to a user-specified time resolution and builds a regression tree for estimating the prediction interval of the output...
Linear regression model is widely used in data stream prediction processing. In order to eliminate the prediction deviation caused by small data set, curve tendency correction technique is used to increase the prediction accuracy. Firstly the weighted moving method is used to modify the prediction function parameters. This algorithm improves the predicting accuracy, but causes low efficiency of time...
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents...
The aim of this paper is to develop a nonlinear orthogonal signal correction (OSC) algorithm using kernel-based technique, termed as kernel OSC (KOSC), and investigate its effects on multivariate calibration. As a nonlinear data pretreatment, the proposed KOSC method can better analyze the nonlinear relationships between descriptor and response variables and remove from process measurement those undesirable...
In this paper, multivariate time series models are built to predict the power ramp rate of a wind farm. The power changes are predicted at ten-minute intervals. Multivariate time series models are built with data-mining algorithms. Five different data-mining algorithms are tested using data collected at a wind farm. The support vector machine regression algorithm performed best of the five algorithms...
Naive Bayes for regression (NBR) uses the naive Bayes methodology to numeric prediction tasks. The main reason for its poor performance is the independence assumption. Although many recent researches try to improve the performance of naive Bayes by relaxing the independence assumption, none of them can be directly applied to the regression framework. The objective of this work is to present a new...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.