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ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES SCHOOL OF INFORMATION SCIENCE A SEMI SUPERVISED APPROACH FOR AMHARIC NEWS CLASSIFICATION BY ANIMUS BELAY ACRES JUNE 2012 ADDIS ABABA UNIVERSITY SCHOOL

How to fill out a semi-supervised approach for

How to fill out a semi-supervised approach for:

1. Identify your dataset: The first step in filling out a semi-supervised approach is to identify the dataset that you will be working with. This dataset should consist of both labeled and unlabeled data. Labeled data refers to instances that have been manually annotated or labeled, while unlabeled data refers to instances that have not been labeled.
2. Split your dataset: Once you have identified your dataset, it is important to split it into labeled and unlabeled subsets. This can be done randomly or based on certain criteria. The labeled subset will be used to train your model, while the unlabeled subset will be used for semi-supervised learning.
3. Train your model with labeled data: Use the labeled subset of your dataset to train a model using supervised learning techniques. This involves providing the model with input features and their corresponding labels, and allowing it to learn patterns and relationships between the features and labels.
4. Use the trained model for predictions: Once your model is trained, you can use it to make predictions on the unlabeled subset of your dataset. These predictions will be based on the patterns and relationships that the model has learned from the labeled data.
5. Incorporate the predictions into your training set: After obtaining predictions for the unlabeled data, you can incorporate these predictions into your training set as "pseudo-labels". Pseudo-labels are temporary labels assigned to the unlabeled instances based on the model's predictions. By doing this, you can potentially improve the performance of your model by utilizing these additional labeled instances.
6. Retrain the model with the augmented training set: After incorporating the pseudo-labels into your training set, retrain your model using the augmented training set. This will allow the model to learn from the newly labeled instances and potentially improve its performance.

Who needs a semi-supervised approach for:

1. Researchers in the field of machine learning: Semi-supervised approaches are particularly useful for researchers in the field of machine learning who are working with large amounts of unlabeled data. These approaches can help leverage the vast amounts of unlabeled data available to improve the performance of their models.
2. Companies with limited labeled data: In cases where obtaining labeled data is time-consuming or expensive, a semi-supervised approach can be beneficial. By making use of both labeled and unlabeled data, companies can train models that perform well even with limited labeled data.
3. Applications involving text or image data: Semi-supervised approaches are commonly used in applications involving text or image data. These approaches can help in tasks such as document classification, sentiment analysis, image recognition, and more, where labeled data may be scarce but unlabeled data is readily available.

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What is a semi-supervised approach for?

A semi-supervised approach is used in machine learning when only some of the data is labeled, and the algorithm must use this partially labeled data to make predictions on the unlabeled data.

Who is required to file a semi-supervised approach for?

Researchers, data scientists, and anyone working on predictive modeling tasks may choose to use a semi-supervised approach in their work.

How to fill out a semi-supervised approach for?

To fill out a semi-supervised approach, one must first label a portion of the data, then use this labeled data to train a model that can make predictions on the unlabeled data.

What is the purpose of a semi-supervised approach for?

The purpose of a semi-supervised approach is to improve the performance of machine learning algorithms when there is limited labeled data available.

What information must be reported on a semi-supervised approach for?

The information reported on a semi-supervised approach includes the labeled data used for training, the model architecture, and the predictions made on the unlabeled data.

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