The idea of predictive modeling is thus gaining support in the counterdrug community. The problem is divided into sea, air and land forecasting, only part of which will be addressed here. The maritime problem is solved using multiple regression in lieu of multivariate time series. This model ...
It uses a well-defined series of steps beginning with image acquisition and continuing with various image-processing tasks such as scaling, filtering, segmentation, feature extraction, and selection, and finally, detection and classification are performed using ML or DL techniques [21]....
We combine the RIR block with the residual block to form a Residual-RIR module, and then a sequence of Residual-RIR modules are stacked to a deep network named region-based integration-and-recalibration network (RIR-Net), to predict NC severity levels automatically. The experiments on a ...
if the dimension shuffle is applied, the LSTM will require M time steps to process Q variables per time step. In other words, the dimension shuffle improves the efficiency of the model when the number of variables M is less than the number of...
Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, an
This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM ( Support Vector Machines ) c... Y Ying,X Wang,B Liu - 《电子科学学刊(英文版)》 被引量: 9发表: 2005年 Training a Φ-Machine Classifier Using Feature Scaling-...
Scaling with instances using out-of-core learning 外存学习是扩展学习能力的一种技术。 其不需要将数据全部加载到内存,可以批量地从外存(例如硬盘)中读取数据,边读取边学习。 (1)流式读取数据。 (2)提取特征。 (3)增量学习。 Out-of-core (or “external memory”) learning is a technique used to learn...
The second way is based on deep learning techniques. Here, the corpus of labeled data is used in order to train the weights of a neural network so that once trained it can infer the label of new data. Often, especially in cases where there is a large amount of data, neural networks ...
In this work, two PCs and all of the ICs are initially used as input to the SVM. Firstly, the PCs or ICs are scaled to the range [−1,+1] in each column to obtain the training data. The main advantage of scaling is to avoid attributes with a greater range dominating those with...
2 PRINCIPAL COMPONENT ANALYSIS PCA is probably the oldest and best known of the techniques used for multivariate analysis. The overall goal of PCA is to reduce the dimensionality of a data set, while simultaneously retaining the information present in the data. Dimensionality reduction or data ...