and . Given a source domain and learning task , a target domain and learning task , where , or , transfer learning aims to help improve the learning of the target predictive function in using the knowledge in and . Applications Algorithms are available for transfer learning in Markov logic networks and Bayesian networks. Transfer learning has also been applied to cancer subtype discovery, building utilization, general game playing, text classification, digit recognition, medical imaging and spam filtering. In 2020 it was discovered that, due to their similar physical natures, transfer learning is possible between electromyographic (EMG) signals from the muscles and classifying the behaviors of electroencephalographic (EEG) brainwaves, from the gesture recognition domain to the mental state recognition domain. It was also noted that this relationship worked vice versa, showing that EEG can likewise be used to classify EMG. The experiments noted that the accuracy of neural networks and convolutional neural networks were improved through transfer learning at both the first epoch (prior to any learning, ie. compared to standard random weight distribution) and the asymptote (the end of the learning process). That is, algorithms are improved by exposure to another domain. Moreover, the end-user of a pre-trained model can change the structure of fully-connected layers to achieve superior performance. Softwares Several compilations of transfer learning and domain adaptation algorithms have been implemented over the past decades: ADAPT (Python) TLlib (Python) Domain-Adaptation-Toolbox (Matlab) See also Crossover (genetic algorithm) Domain adaptation General game playing Multi-task learning Multitask optimization Zero-shot learning References Sources Machine learning