Search Results for author:
Found 5 papers, 1 papers with code
Deep Evolution for Facial Emotion Recognition
Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability.
Automated Classification of Text Sentiment
The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans.
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Deep neural networks continue to show improved performance with increasing depth, an encouraging trend that implies an explosion in the possible permutations of network architectures and hyperparameters for which there is little intuitive guidance.
Text Compression for Sentiment Analysis via Evolutionary Algorithms
An analysis of PARSEC with eight commercial and non-commercial sentiment analysis algorithms on twelve English sentiment data sets reveals that accurate compression is possible with (0%, 1. 3%, 3. 3%) loss in sentiment classification accuracy for (20%, 50%, 75%) data compression with PARSEC using LingPipe, the most accurate of the sentiment algorithms.
Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks
We propose the Automated Problem Identification (API) algorithm, which uses an evolutionary algorithm interface to TensorFlow to manipulate a deep neural network to decide if a dataset represents a classification or a regression problem.
