semi supervised learning using gaussian fields and harmonic functions pdf

Semi Supervised Learning Using Gaussian Fields And Harmonic Functions Pdf

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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.

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

Semi-supervised Learning

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.


Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

This paper describes the process of automatic identification of concepts in different languages using a base that relies on simple semantic and morphosyntactic characteristics like string similarity, difference in words amount and translation position on dictionary when exists and a neural network that has been used as a model of machine learning. The results were compared with dictionary and showed that the introduction of neural network brought a significant gain in the process of equivalence of concepts. Resumo This paper describes the process of automatic identification of concepts in different languages using a base that relies on simple semantic and morphosyntactic characteristics like string similarity, difference in words amount and translation position on dictionary when exists and a neural network that has been used as a model of machine learning. Duarte M. Mitchell, T.

Handbook on Neural Information Processing pp Cite as. However, labeling the training data for real-world applications is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience and training. There are implicit costs associated with obtaining these labels from domain experts, such as limited time and financial resources. This is especially true for applications that involve learning with large number of class labels and sometimes with similarities among them. Semi-supervised learning SSL addresses this inherent bottleneck by allowing the model to integrate part or all of the available unlabeled data in its supervised learning.

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 Using Gaussian Fields and Harmonic Functions

2 comments

Jerajcipar

In many traditional approaches to machine learning, a tar- get function is estimated using labeled data, which can be thought of as examples given by a “​teacher”.

REPLY

Riihanzsweetsu

Corpus ID: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.

REPLY

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