Semi Supervised Learning Using Gaussian Fields And Harmonic Functions Pdf
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- Introduction to Semi-Supervised Learning
- Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
- Semi-Supervised Learning Software
- Semi-supervised Learning
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. We discuss methods to incorporate class priors and the predictions of classifiers obtained by supervised learning.
Introduction to Semi-Supervised Learning
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Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm e. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation.
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PDF | Graph-based semi-supervised learning (SSL) algorithms have gained increased attention in is the Gaussian Fields and Harmonic Functions (GFHF), which learn from both labeled and unlabeled examples using a.
Semi-Supervised Learning Software
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Semi supervised learning github. Labeled data is a scarce resource. A standard choice for the LabelSpreading model for semi-supervised learning This model is similar to the basic Label Propgation algorithm, but uses affinity matrix based on the normalized graph Laplacian and soft clamping across the labels.
We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semi-supervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error risk ; in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection..
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Semi-supervised learning constructs the predictive model by learning from a few labeled training examples and a large pool of unlabeled ones. It has a wide range of application scenarios and has attracted much attention in the past decades. However, it is noteworthy that although the learning performance is expected to be improved by exploiting unlabeled data, some empirical studies show that there are situations where the use of unlabeled data may degenerate the performance. Thus, it is advisable to be able to exploit unlabeled data safely. This article reviews some research progress of safe semi-supervised learning, focusing on three types of safeness issue: data quality, where the training data is risky or of low-quality; model uncertainty, where the learning algorithm fails to handle the uncertainty during training; measure diversity, where the safe performance could be adapted to diverse measures.
An approach to semi-supervised learning is pro- posed that is based on a Gaussian random field model. Labeled Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. January Request Full-text Paper PDF. To read.