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Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and contains multiple fields, a typicalrepresentation is to transform it into a high-dimensional sparsebinary feature representation via one-hot encoding...
Wind speed forecasting is critical to and challenging for wind energy industry. We present a combined AR-kNN regression model for short-term wind speed forecasting. Historical samples are selected to train the coefficients of a k-nearest-neighbor (kNN) regression model in order to capture the current variation pattern of wind speed. The training samples of the kNN model are combined with the recent...
In many practical machine learning systems, the prediction/classification tasks involve the usage of heterogeneous data in semi-supervised settings, where the objective is to maximize the utility of multiple views (usually dual views) information from the data. In this work, we propose a general framework, Dual Uncertainty Minimization Regularization (DUMR), that maximizes the usage of heterogeneous...
Defects in every software must be handled properly, and the number of defects directly reflects the quality of a software. In recent years, researchers have applied data mining and machine learning methods to predicting software defects. However, in their studies, the method in which the machine learning models are directly adopted may not be precise enough. Optimizing the machine learning models...
Web documents can be clustered into topics with topic detection and tracking(TDT) technologies. With the topics' data collected by TDT system, it is found that the lifecycle of topics has 4 stages. In the paper a HMM-based state prediction model for topics is proposed. Some topics with similar lifecycles share a same model, several models are trained with history data of topics, these models are used...
Various attribute and relation information is used in social recommendation systems. However, previous approaches fail to use them in a unified way. In this paper, we propose a unified framework for social recommendation. Entities like users and items are described by their tags. We model each entity using topic models like Latent Dirichlet Allocation(LDA) and then connect these topic models to form...
Linear auto-regression moving average with extra input (ARMAX) model for air temperature system in a naturally ventilated greenhouse was developed. Outside air temperature, relative humidity, global solar radiation and wind speed were used as disturbing input variables of the model, while inside temperature was used as output variable of the model. Based on energy and mass flows equations, statistic...
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