Application of meta-learning in machine learning

Meta-learning helps machine learning algorithms overcome challenges by optimizing learning algorithms and identifying the best-performing algorithms.

Meta-learning, meta-classifiers and meta-regression

Meta-classifiers in machine learning

Meta classifier is a meta-learning algorithm in machine learning and is used for classification and prediction modeling tasks. It uses the results predicted by other classifiers as features and finally selects one of them as the final prediction result.

Meta-regression

Meta-regression is a meta-learning algorithm used for regression predictive modeling tasks. It uses regression analysis to combine, compare, and synthesize findings from several studies while adjusting for the effect of available covariates on the response variable. Meta-regression analyzes aim to reconcile conflicting studies or confirm studies that are consistent with each other.

What techniques are used in meta-learning?

The following are some methods used in meta-learning:

• Metric Learning

This refers to learning a metric space for prediction. It provides good results in few-shot classification tasks. The main idea of metric learning is very similar to the nearest neighbor algorithm (k-NN classifier and k-means clustering).

• Model-agnostic meta-learning (MAML)

In MAML, neural networks are trained using examples , so that the model can adapt to new tasks faster. It is a general optimization and task-agnostic algorithm used to train the parameters of a model for fast learning with a small number of gradient updates.

• Recurrent Neural Network (RNN)

Recurrent neural network is a type of artificial intelligence. They are good at dealing with problems with sequential or time series data. You'll typically find them used for language translation, speech recognition, and handwriting recognition tasks.

In meta-learning, the RNN algorithm is used as an alternative to creating recurrent models that are able to sequentially collect data from a dataset and process these data as new inputs.

• Stacking or Stacked Generalization

Stacking is a subfield of ensemble learning used for meta-learning models. Both supervised and unsupervised learning gain advantages from stacking. The process involved in stacking: training a learning algorithm using the available data, creating a combiner algorithm to combine the predictions of the learning algorithm, and using the combiner algorithm to make the final prediction.

• Convolutional Siamese Neural Network

Convolutional Siamese Neural Network consists of two twin networks. Their outputs are trained jointly on top using a function to learn the relationship between pairs of input data samples. Siamese networks share the same weights and network parameters. They refer to the same embedding network, which learns efficient embeddings to reveal relationships between pairs of data points.

• Matching Network

Matching network learns a classifier for any small support set. A classifier defines a probability distribution of output labels using specific test examples. It essentially maps a small tagged support set and an untagged example to its label, eliminating the need for fine-tuning to accommodate new class types.

• LSTM meta-learner

The LSTM meta-learning algorithm can find accurate optimization algorithms for working with few samples mechanism to train another learner neural network classifier. Parameterization allows it to learn appropriate parameter updates, specifically for scenarios where a certain number of updates will be made. It can even learn a general initialization of the learner network to achieve fast convergence of training.

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