What is Semi-supervised Machine Learning?

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Semi-supervised learning is a machine learning paradigm that falls between supervised and unsupervised learning. In semi-supervised learning, the dataset contains a mixture of labeled and unlabeled data points. The goal is to leverage both types of data to build predictive models or extract useful information.

Here's an overview of semi-supervised learning:

Labeled Data:
Labeled data consists of input samples paired with corresponding output labels or target values. These labeled data points are used to train supervised learning models, where the algorithm learns to make predictions based on input-output pairs.
Unlabeled Data:
Unlabeled data consists of input samples without corresponding output labels. Unlike supervised learning, where every data point is labeled, unlabeled data is abundant and often easier to acquire in real-world scenarios.
Model Training:
In semi-supervised learning, the algorithm utilizes both labeled and unlabeled data during the training process. The labeled data is used in a similar manner to supervised learning, where the algorithm learns from labeled examples to make predictions or extract patterns.
Additionally, the unlabeled data is leveraged to improve the model's performance by incorporating information from the unlabeled samples into the learning process. This can help the model learn a better representation of the underlying data distribution and generalize more effectively to unseen data.
Pseudo-labeling:
One common approach in semi-supervised learning is pseudo-labeling, where the model makes predictions on the unlabeled data and assigns pseudo-labels to these predictions. The labeled and pseudo-labeled data are then combined to train the model in a supervised manner.
Pseudo-labeling can help the model utilize the information contained in the unlabeled data to improve its performance, especially in scenarios where labeled data is scarce or expensive to obtain.
Co-training:
Another approach in semi-supervised learning is co-training, where multiple models are trained on different subsets of features or views of the data. Each model is trained on a combination of labeled and unlabeled data, and they exchange information during the training process to improve each other's performance.
Co-training is particularly useful when the dataset contains multiple modalities or sources of information that can be leveraged to enhance the learning process.

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