Semi-supervised in Machine Learning

So What is Semi-supervised actually is ? Any wonder what is Semi-supervised is ? then i’ll share my thoughts here..

First of all What is Machine Learning ?

My first thought coming form the movie like the robot that full of Ai and stuff starts to kill the human lol,

Long story short i did a little research to it, so basically AI is like the program that made for either understanding human being, to help human activities and so.

Or simply AI is a program that can sense, reason, act, and adapt. AI has a several sub division, one of them is Machine Learning.

Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”

Machine Learning algorithms can be broadly classified into three categories, Supervised Learning, Unsupervised Learning and Reinforcement Learning. Casting Reinforced Learning aside, the primary two categories of Machine Learning problems are Supervised and Unsupervised Learning. The basic difference between the two is that Supervised Learning datasets have an output label associated with each tuple while Unsupervised Learning datasets do not. From both Supervised and Unsupervised, it has disadvantages, to counter those disadvantages, the concept of Semi-Supervised Learning was introduced. . In this type of learning, the algorithm is trained upon a combination of labeled and unlabeled data. A Semi-Supervised algorithm assumes the following about the data –

1. Continuity Assumption: The algorithm assumes that the points which are closer to each other are more likely to have the same output label.
2. Cluster Assumption: The data can be divided into discrete clusters and points in the same cluster are more likely to share an output label.
3. Manifold Assumption: The data lie approximately on a manifold of much lower dimension than the input space. This assumption allows the use of distances and densities which are defined on a manifold.

Practical applications of Semi-Supervised Learning –

1. Speech Analysis: Since labeling of audio files is a very intensive task, Semi-Supervised learning is a very natural approach to solve this problem.
2. Internet Content Classification: Labeling each webpage is an impractical and unfeasible process and thus uses Semi-Supervised learning algorithms. Even the Google search algorithm uses a variant of Semi-Supervised learning to rank the relevance of a webpage for a given query.
3. Protein Sequence Classification: Since DNA strands are typically very large in size, the rise of Semi-Supervised learning has been imminent in this field.

You can learn more about it here. That is all from me, wish you good luck..