Semi Supervised Support Vector in 60 Seconds | Machine Learning Algorithms
📺 Semi Supervised Support Vector in 60 Seconds | Machine Learning Algorithms
📖 The Hitchhiker's Guide to Machine Learning Algorithms | by @serpdotai
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Semi-Supervised Support Vector Machines (S3VM) are like a chef cooking a delicious meal. The chef has some ingredients that they know how to cook and have a recipe for (labeled data), but also has several new ingredients they have never cooked with before (unlabeled data). Instead of throwing away the unknown ingredients, the chef wants to figure out how to best use them to enhance the meal. The chef would use the labeled ingredients as a starting point, and then use the new ingredients to improve the flavor and texture of the dish.
S3VM is an extension of Support Vector Machines (SVM) for semi-supervised learning. SVMs are classification algorithms that use a set of training data to create decision boundaries between different classes. S3VM makes use of a large amount of unlabelled data and a small amount of labeled data to perform classification tasks. The aim is to leverage the unlabelled data to improve the decision boundary constructed from the labeled data alone.
Imagine a teacher trying to assign grades to all of their students. If the teacher only had the grades for a few students, they would have a difficult time determining the overall grade distribution of the class. But if the teacher had access to the previous year's grades for the same class, they could use this additional data to better estimate the grades for the new students.
S3VM is especially useful when labeled data is scarce or expensive to obtain. By using a combination of labeled and unlabeled data, S3VM creates a more accurate decision boundary and improves the overall classification performance. It is a type of instance-based learning algorithm that falls under the category of semi-supervised learning.
Think of S3VM as a chef trying to make the best dish possible with both familiar and unfamiliar ingredients, or a teacher trying to assign grades to students with limited information. By leveraging both labeled and unlabeled data, S3VM can perform better classification tasks.
Semi-Supervised Support Vector Machines, also known as S3VM, is an instance- based algorithm and an extension of Support Vector Machines (SVM) for semi- supervised learning.
This algorithm is designed to make use of a large amount of unlabelled data and a small amount of labelled data to perform classification tasks. The aim is to leverage the unlabelled data to improve the decision boundary constructed from the labelled data alone. This approach is especially useful when labelled data is scarce or expensive to obtain.
Semi-Supervised Support Vector Machines can be categorized as a Semi- Supervised Learning method, and it has been extensively used in a variety of applications, including image and text classification.
In the following sections, we will dive deeper into how this algorithm works and explore its strengths and weaknesses.
Semi-Supervised Support Vector Machines: Use Cases & Examples
Semi-Supervised Support Vector Machines (S3VM) is an extension of Support Vector Machines (SVM) for semi-supervised learning. It is an instance-based algorithm that aims to leverage a large amount of unlabelled data and a small amount of labelled data to perform classification tasks. S3VM is especially useful when labelled data is scarce or expensive to obtain.
The main advantage of S3VM is its ability to improve the decision boundary constructed from the labelled data alone by incorporating the unlabelled data. This algorithm has been successfully applied in various fields such as image classification, natural language processing, and bioinformatics.
One example of the use of S3VM is in the field of image classification. In a study conducted by Chen et al. (2016), S3VM was used to classify images of different plant species based on their leaf shapes. The algorithm was able to achieve high accuracy even with a small amount of labelled data, demonstrating its effectiveness in situations where labelled data is limited.
Another example of the use of S3VM is in natural language processing. In a study conducted by Zhu et al. (2015), S3VM was used to automatically classify Chinese news articles into different categories. The algorithm was able to achieve high accuracy by leveraging the unlabelled data, demonstrating its usefulness in situations where labelled data is expensive to obtain.
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