Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2021; 22nd International C Hujun Yin,David Camacho,Susana Nascimento Conference

ETHER

An Empirical Study of the Impact of Field Features in Learning-to-rank Methodocuments and naively combined. However, such a conventional way to learn a ranking model does not accurately reflect the utility and contribution of the fields and may also risk joining highly correlated features from different fields. It lacks an empirical analysis of how field-grouped features det

ingenue

神刊

A Parallel Variable Neighborhood Search for Solving Real-World Production-Scheduling Problemscific tools to be correctly produced. It is also relevant to underscore that the problem solved in this research corresponds to a real-world situation, and that it is currently deployed in a production plant in the Basque Country (Spain).

過(guò)度

A Neural Architecture for Detecting Identifier Renaming from Diffll source code before and after the commit, which is less efficient and requires rigorous syntactical completeness and correctness, our novel approach based on neural networks is able to read only the diff and gives a confidence value of whether it is a renaming or not. Since there had been no exist

表示問(wèn)

Ensemble Synthetic Oversampling with Manhattan Distance for Unbalanced Hyperspectral Datag performance and introduce bias in performance measurement. In this paper, an oversampling method is proposed based on Safe-Level synthetic minority oversampling technique (Safe-Level SMOTE), which is modified in terms of its k-nearest neighbours (KNN) function to make it fit better with high dimen

跳脫衣舞的人

大漩渦

Application of Long Short-Term Memory Neural Networks for Electric Arc Furnace Modellingms. Next, we developed two models using long short-term memory artificial neural network (LSTM) for recreating the time series of the coefficients while remaining their stochastic properties. The second model also applies another LSTM for the reduction of stochastic-like residuals emerging from comp

Flustered

An Empirical Study of the Impact of Field Features in Learning-to-rank Methodenchmark datasets, show that ranking models learned using field-grouped features have competitive advantages over the models learned using a naively combined feature list, and that aggregation results of different fields present a better performance. These results suggest that learning ranking model

CLOT

MAUVE