Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms not suitable for large-scale datasets due to their stringent data storage requirement. Online have been devised tackle this problem by concurrently training with incoming samples and providing inference results. However, even most up-t...

To address the problems of existing online education curriculum recommendation methods such as low accuracy, an course algorithm (BTCBMA) considering learner learning quality is proposed. Firstly, BERT model combined with TextCNN to implement preliminary extraction text features. Secondly, convolution neural networks and BiLSTM are used capture deep features temporal in data. Finally, a multi-h...

the need for intercultural awareness and skills emerges strongly in both distance learning courses, and in social life in multicultural societies. the study of online language transactions is therefore an important aspect of the emerging culture and sociolinguistics of computer mediated communication. the research reported in this paper concerns perceptions held by students in an iranian univer...

We propose a state-based variant of the classical online learning problem of tracking the best expert. In our setting, the actions of the algorithm and experts correspond to local moves through a continuous and bounded state space. At each step, Nature chooses payoffs as a function of each player’s current position and action. Our model therefore integrates the problem of prediction with expert...

Learning Bayesian network is a problem to obtain a network that is the most appropriate to training dataset based on the evaluation measures given. It is studied to decrease time and effort for designing Bayesian networks. In this paper, we propose a novel online learning method of Bayesian network parameters. It provides high flexibility through learning from incomplete data and provides high ...

The emergence of ubiquitous sources of streaming data has given rise to the popularity of algorithms for online machine learning. In that context, Hoeffding trees represent the state-of-the-art algorithms for online classification. Their popularity stems in large part from their ability to process large quantities of data with a speed that goes beyond the processing power of any other streaming...

Corresponding Author: Binjie Gu Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, China Email: [email protected] Abstract: In order to model real fermentation process, a soft sensor modelling of biomass concentration during fermentation using accurate incremental online ν-Support Vector Regression (ν-SVR) learning algorithm was p...

The true online TD(λ) algorithm has recently been proposed (van Seijen and Sutton, 2014) as a universal replacement for the popular TD(λ) algorithm, in temporal-difference learning and reinforcement learning. True online TD(λ) has better theoretical properties than conventional TD(λ), and the expectation is that it also results in faster learning. In this paper, we put this hypothesis to the te...

Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform traditional RNNs when dealing with sequences involving not only short-term but also long-term dependencies. The decoupled extended Kalman filter learning algorithm (DEKF) works well in online environments and reduces significantly the number of training steps when compared to the standard gradient-descent algorithms. Prev...