You can use the layer activations as features to train another machine learning model (such as a support vector machine (SVM)). Or you can use the pretrained model as a building block for another deep learning
You extract learned features from a pretrained network, and use those features to train a classifier, for example, a support vector machine (SVM — requires Statistics and Machine Learning Toolbox™). For example, if an SVM trained using a SqueezeNet neural network can achieve over 90% ...
Support vector machine (SVM)Design tool for CNN and SVMMATLABThis paper presents a user-friendly design application development environment based on MATLAB that facilitates two applications using convolutional neural networks (CNNs) and support vector machines (SVMs). Firstly, an application of deep ...
To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted ...
Once the features are extracted, you can train and evaluate various machine learning algorithms, such as support vector machine (SVM) and random forest, or deep learning algorithms, such as long short-term memory (LSTM) networks. Show more Published:...
《Machine learning open source software》 介绍:机器学习开源软件,收录了各种机器学习的各种编程语言学术与商业的开源软件.与此类似的还有很多例如:DMOZ - Computers: Artificial Intelligence: Machine Learning: Software, LIBSVM -- A Library for Support Vector Machines, Weka 3: Data Mining Software in Java,...
The learned time-frequency embeddings reduce the dimensionality of the time-series data by a factor of 16. You can use these embeddings to classify EEG time-series from persons with and without epilepsy using a support vector machine classifier....
(2560 sections), features were extracted by hybridization of Darknet53, InceptionV3, and Resent101 models and concatenated. Then, they were optimized by mPMR and classified by support vector machine (SVM) and k-nearest neighbors (KNN) and showed very high accuracy of Alzheimer’s disease ...
In MATLAB, you can perform Taylor pruning by using a taylorPrunableNetwork object. Implement the iterative pruning process in a custom training loop. Calculate the importance of the learnable parameters using the updateScore function. Remove the n least important learnable parameters using the update...
For more information on build, see this post inMATLAB Answers™:https://www.mathworks.com/matlabcentral/answers/447387-matlab-coder-how-do-i-build-the-intel-mkl-dnn-library-for-deep-learning-c-code-generation-and-dep ARM Compute Library for computer vision and machine learning, versions 18.11...