About Deep Learning and Meta-Learning Algorithm

the stacking algorithm works:

  1. Data Preparation: The training dataset is divided into two or more subsets. For example, let’s consider three subsets: A, B, and C.

  2. Base Model Training: Each base model is trained on a different subset of the training data. For instance, model 1 is trained on subset A, model 2 on subset B, and model 3 on subset C. These base models can be any machine learning algorithms, such as decision trees, random forests, support vector machines, or neural networks.

  3. Base Model Predictions: Once the base models are trained, they are used to make predictions on a validation set that was not used during their training. The validation set could be a different subset of the original training data or a completely separate dataset. The predictions made by each base model represent their individual strengths and weaknesses.

  4. Meta Model Training: A meta model, is trained using the predictions made by the base models as inputs. The meta model learns to combine the base models’ predictions to make a final prediction. The meta model is trained on the validation sets along with their corresponding ground truth labels.

  5. Final Prediction: Once the meta model is trained, it can be used to make predictions on new, unseen data. The base models first make predictions on the new data, and then the meta model takes these predictions as input and produces the final prediction.

By combining the predictions of multiple base models and learning from their strengths and weaknesses, the stacking algorithm aims to improve the overall predictive accuracy and robustness of the ensemble model.

In deep learning algorithms, a similar process occurs, but without dividing the datasets into subsets (CVs) for training and prediction. Instead, the model trains on the entire dataset and makes predictions using activation functions like sigmoid, relu, and tanh.

The base models in the stacking algorithm can be compared to neurons in deep learning, but the main difference is that the model is uniform for all neurons on each layer. On the second hidden layer, the meta-models take over and are trained on the output of the first hidden layer. In the third hidden layer, we use meta-models of meta-models outputs, which take the outputs of the previous hidden layer as input. The final output is also considered a meta-model.of meta_model outputs of the last hidden layer.

Overall, the stacking algorithm and deep learning algorithms share similarities in their hierarchical structure, but they differ in how the models are trained and how predictions are made.

What happens if we reset the assumption of a uniform activation function for each layer and allow each neuron to have its own activation function? Will this improve the performance?
Has anyone done this experiment yet? if so please give me the article link
thanks in advance

I havent done this but here is what I think. The concept of having similar activations in repeated patterns like the layes is to automate the process.

The neural networks introduce non-linearities which is the essence of fitting complex phenomena, you have the possibility of fitting any complex phenomena with inceasing NN size and architecture.

Can you have better activations, more intelligent solutions, oh yes, but this comes at the cost of management and complexity in many dimensions.

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i think
If you reset the assumption of a uniform activation function for each layer and allow each neuron to have its own activation function, it can potentially improve the performance of the model. The reason is that different activation functions have different properties and can capture different types of patterns or relationships in the data.

By allowing each neuron to have its own activation function, you are giving more flexibility to the model to learn complex and diverse representations. Some neurons may benefit from using a sigmoid activation function. Other neurons may benefit from using a relu activation function and so on.

i don’t know i just guess