[PDF] Top 20 Radar Emitter Recognition based on Transfer Learning

... The classic K-means and DBSCAN algorithms are used to cluster the data sets in this paper. There are large fluctuations although the K-means algorithm is the average of many experiments, and the clustering effect depends ... See full document

... material recognition. This paper proposes a novel FOD material recognition approach based on both transfer learning and a mainstream deep convolutional neural network (D-CNN) ... See full document

... Z. Yang and et. al, have proposed a CNN-based strategy named RFI-Net to detect interference in a five-hundred-meter Aperture Spherical radio Tele-scope (FAST) [14], that can outperform other techniques such as the ... See full document

... novel transfer learning-based microscopic image recognition method for ...with transfer learning utilizing knowledge gained by parasitologists that Toxoplasma is banana or ... See full document

... is based on a sequence labeling sys- tem that uses a bidirectional LSTM (Hochreiter and Schmidhuber, 1997) which extracts features for training a Conditional Random Field (Sut- ton and McCallum, ...fer ... See full document

... and transfer it for learning the new objects ...in learning the new objects through their similarity and connection with the new ...objects. Based on this idea, some studies suggest that the ... See full document

... the transfer learning setting (Dai et ...2007). Transfer learning has been applied to the SVM framework, during the training process of the target detector the previously learned template is ... See full document

... In most speech recognition tasks, performance of trad- itional systems still triumph end-to-end approaches [4– 7]. Many published results have shown that the perform- ance gap between them shrinks with greater ... See full document

... use transfer learning to deal with NER data sets with different label ...labels based on the k nearest neighbours of each label type, and from this transferring a pre-trained model from the source to ... See full document

... deep learning model from scratch requires huge amount of data, high computational resources, and hours, in some cases days, of ...deep learning models for such ...for learning new objects ...the ... See full document

... introduce transfer learning from multiple sources in the two-stream networks, which can transfer still image and motion features from ImageNet and an action recognition dataset respectively, ... See full document

... A further analysis of the results on the internal datasets showed that the frequency of a tag class in the target training data correlated the most with TL gain. This is visualized in Figure 4 for a subset of the JP ... See full document

... pattern recognition training through software programs as well as paper-pencil mode along with behavioural techniques was helpful for all the cases of learning ...with learning disability for making ... See full document

... It has recently been shown that for emotion recognition, deep learning based systems learn features that outperform handcrafted features (Tri- georgis et al., 2016) (Martinez et al., 2013) (Kim et ... See full document

... emotion recognition has been widely studied, as one of the first affective computing techniques, mainly based on visual features of the face expression combining features about eyes, mouth and various ... See full document

... So far, empirical mode decomposition method has been successfully applied in many fields, the actual effect is very significant. Because there is no a priori requirement of the conditions, it has a very good adaptive ... See full document

... the radar emitter ...method based on rough sets theory and radial basis function (RBF) neural net- ...a radar emitter recognition method using the single parameter dynamic search ... See full document

... and emitter identification (EID) problems are most appropriate for ELINT equipment ...pattern recognition methods usually explore statistical properties in the data set and they perform well when such ... See full document

... To enter the main part of the gesture recognition algorithm, a human has to first stand still for a few seconds (to distinguish from people who just walk on by). After that, this human has to put out his hand ... See full document

... the transfer learning problem is to use a prior which, in conjunc- tion with a probabilistic model, allows one to spec- ify a priori beliefs about a distribution, thus bias- ing the results a ... See full document