How to use unlabeled data to train a classifier?
Hence in the first step, use the labeled data to train a classifier. In the second step, apply it to the unlabeled data for class probabilities labeling (the “expectation” step). In the third step, use all data labels to train a new classifier (the “maximization” step).
How are unlabeled data used in classification task?
However, in this way, coupling naïve Bayes and EM works well in the classification of documents. Employing less than one-third of the labeled training instances as well as five times as many unlabeled ones, it can achieve the performance of a traditional learner in a particular classification task.
How is text classification different from text clustering?
Text classification is a problem where we have fixed set of classes/categories and any given text is assigned to one of these categories. In contrast, Text clustering is the task of grouping a set of unlabeled texts in such a way that texts in the same group (called a cluster) are more similar to each other than to those in other clusters.
Is it possible to do manual text classification?
Manual text classification involves a human annotator, who interprets the content of text and categorizes it accordingly. This method can deliver good results but it’s time-consuming and expensive.
Is it possible to do classification without data?
You can’t do classification without labeled data, of course, because only the labels tell what the classes are. But it is sometimes attractive to augment a small amount of labeled data with a large pool of unlabeled data. It turns out that the unlabeled data can help you learn the classes.
How can naive Bayes be used to train a classifier?
The idea is to employ naïve Bayes to utilize the EM iterative clustering algorithm to learn classes from a small, labeled dataset and then extend it to a large, unlabeled dataset. Hence in the first step, use the labeled data to train a classifier.