pyoselm
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leferrad
A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning
e.g. replace "sigmoid" with this one: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.expit.html
A BatchNormalization layer could be a good idea to avoid overflows. Also, Dropout could be something useful (to be checked)
ELMClassifier inherits from ELMRegressor, so GenELMClassifier should not be needed
A good option would be a simple Jupyter notebook, where it could show the main features to let the user understand how to completely use this library
Assert correct behavior of OSELMClassifier in sequential learning scenarios with feed of new classes
Let's say the model was trained with classes 1-N, and then a batch of data comes to a fit() call with a new class N+1. The model should incorporate this...
This library could have a new feature: a module `optimization` with methods to try different hyper-parameters settings to get the best configuration of a model for a given dataset X,...
In order to increase the learning capacity of a model, it would be convenient to think in architectures of multiple layers. Check this paper: https://www.hindawi.com/journals/mpe/2017/4670187/
All the calls to `pinv2` function could raise errors if a singular matrix is operated. We could either identify these cases or just try/catch them to raise a proper error...
--------------------------------------------------------------------------- ImportError Traceback (most recent call last) [F:\TEMP\ipykernel_234664\765891537.py](file:///F:/TEMP/ipykernel_234664/765891537.py) in () ----> 1 from pyoselm import OSELMRegressor, OSELMClassifier 2 from sklearn.datasets import load_digits, make_regression 3 from sklearn.model_selection import train_test_split 4 5...
