P300 speller is a system that allows users to input words using electroencephalogram (EEG). A component called P300 is used to interpret the EEG in P300 speller. In order to make a high performance P300 speller, it is...
详细信息
ISBN:
(纸本)9781467393607
P300 speller is a system that allows users to input words using electroencephalogram (EEG). A component called P300 is used to interpret the EEG in P300 speller. In order to make a high performance P300 speller, it is essential to discriminate P300 from nonP300 precisely and automatically. In this study, deep learning (DL) is used to discriminate P300. the experimental result shows that DL was possible to discriminate P300 in EEG data, especially in the higher level layer. Furthermore, this study refers to the extracted feature by DL. We can see that DL learns feature from the waveforms correctly to discriminate P300 from others.
Heart disease is a serious health problem that has afflicted a lot of people all over the world. In our work, we have proposed a GUI-based machinelearning-basedapproach that is efficient and accurate for identifying ...
详细信息
Partial Memory learning (PML) is a machinelearning paradigm in which only a subset of cases generated from an original training set is used for classification. this paper concerns a new method for partial memory lear...
详细信息
ISBN:
(纸本)3540221239
Partial Memory learning (PML) is a machinelearning paradigm in which only a subset of cases generated from an original training set is used for classification. this paper concerns a new method for partial memory learning. the SBL-PM-M method is a completely new model. We evaluate the performance of the new algorithm on several real-world datasets and compare it to a few other PML systems and to the base classifier.
the issue of difficulties in controlling the inventory of small retailers with limited space and limited capital to buy just a few merchandises has just been led due to the continuous increase in the variety of produc...
详细信息
the goal of the research on fetal health categorization using machinelearning is to create a model that can precisely predict the condition of a fetus during pregnancy. this is crucial because prompt action following...
详细信息
the recognition of bird calls has been a challenging task in the field of bioacoustics. Withthe advancement of machinelearning algorithms, automatic bird call recognition has become an active research area. In this ...
详细信息
Withthe advancement of technologies and in order to pace withthe digital era, robust automated job recommendation systems need to be implemented which overcome the limitations of traditional systems and manual appro...
详细信息
Accurate prediction for inbound tourism demand is important for development and implement of Chinese inbound tourism strategy. It has positive significance. BP neural network as a common traditional machinelearning m...
详细信息
ISBN:
(纸本)9781479966363
Accurate prediction for inbound tourism demand is important for development and implement of Chinese inbound tourism strategy. It has positive significance. BP neural network as a common traditional machinelearning methods is widely used in travel demand forecasting model. However, BP neural network suffers from several drawbacks, such as overfitting, difficulties in setting parameters and local minima problem. Hence the performance of BP neural network is very unstable in practical applications. this paper combines an advanced machinelearning paradigm named ensemble learning with BP neural network to build neural network ensemble for tourism demand prediction. the state-of-art methods for predictive modeling used in tourism research include traditional statistical methods, softcomputing methods, and artificial intelligence methods. Note that artificial intelligence methods, which were introduced to tourism research in 1990s, have greatly improved the predictive accuracy of modeling methods. this study conducts tourism demand modeling of three important tourist source countries of US, Britain and Australia. the results show that, neural network ensemble significantly improves the predictive accuracy over traditional statistical methods and traditional machinelearning methods including single BP neural network. Such method provides a better choice for more accurate predictive modeling for tourism demand of China.
In this paper, we investigate proper strategies for determining the step size of the backpropagation (BP) algorithm for on-line learning. It is known that for off-line learning, the step size can be determined adaptiv...
详细信息
ISBN:
(纸本)9781467393607
In this paper, we investigate proper strategies for determining the step size of the backpropagation (BP) algorithm for on-line learning. It is known that for off-line learning, the step size can be determined adaptively during learning. For on-line learning, since the same data may never appear again, we cannot use the same strategy proposed for off-line learning. If we do not update the neural network with a proper step size for on-line learning, the performance of the network may not be improved steadily. Here, we investigate four strategies for updating the step size. they are 1) constant, 2) random, 3) linearly decreasing, and 4) inversely proportional, respectively. the first strategy uses a constant step size during learning, the second strategy uses a random step size, the third strategy decreases the step size linearly, and the fourth strategy updates the step size inversely proportional to time. Experimental results show that, the third and the fourth strategies are more effective. In addition, compared withthe third strategy, the fourth one is more stable, and usually can improve the performance steadily.
the proposed ensemble learning framework integrates diverse machinelearning algorithms. Each base model is trained on a diverse set of features derived from comprehensive patient data. To evaluate the ensemble model&...
详细信息
暂无评论