Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. The acquisition of labeled data for a learning problem often requires a skilled human agent (e.g. to transcribe an audio segment) or a physical experiment (e.g. determining the 3D structure of a protein or determining whether there is oil
Semi-Supervised Learning Lecture Notes and Tutorials PDF
Page: 156, File Size: 2.48M, Date: 2007
In this tutorial we will learn how to use unlabeled data to improve classification. Xiaojin Zhu (Univ. Wisconsin, Madison). Semi-Supervised Learning Tutorial.by X Zhu · Cited by 52 · Related articles
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INTRODUCTION model and its parameter estimation, we show in detail that each of the semi-supervised learning algorithms reviewed in Chapter 2 is equivalent ...by Y Xu · 2010 · Related articles
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This is referred to as multi-task learning (MTL), and is implemented ... supervised algorithm, exploiting graph theory, with the final algorithm appropriate ... Multi-task learning has been the focus of much recent interest in the machine learning.
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unsupervised learning problems, as their goal is to infer the structure of unlabeled data. Finally, we should note that there are a plethora of other subfields and.by L Melas-Kyriazi · 2020 · Related articles
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People have remarkable abilities to learn concepts from very limited data, often just one or a few labeled examples per class. Semi-supervised learning ...by CC Kemp · Cited by 83 · Related articles
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But it does show that for some relatively natural classes of prediction functions over the real line, semi-supervised learning does not help significantly unless ...by TT Lu · Cited by 19 · Related articles
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In this chapter, we present an algorithmic framework for semi-supervised inference based on geometric properties of probability distributions. Our approach ...
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INTRODUCTION. The focus of this paper is mathematical programming approaches to semi- supervised learning for classification tasks. The main idea of semi- ...by A Demiriz · Cited by 69 · Related articles
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Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; ... such example is ADWIN, an adaptive sliding window approach for drifting data ...
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Keywords: semi-supervised learning, manifold learning, graph regularization, laplace ... Note that both represent the same underlying manifold structure but with.by M BELKIN · 2004 · Cited by 880 · Related articles
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Mar 30, 2018 — and constructs a function f which predicts a label y for an unseen test ex- ample x, i.e., y = f(x). In semi-supervised learning (SSL) the learner ...by WW Cohen · 2018 · Related articles
Page: 31, File Size: 271.97kb, Date: 2004
Keywords: semi-supervised learning, manifold learning, graph regularization, laplace ... Note that both represent the same underlying manifold structure but with.by M BELKIN · 2004 · Cited by 887 · Related articles
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1 Introduction. Topic modeling based on Latent Dirichlet Allocation (LDA) [6] has become a popu- lar tool for data exploration, dimensionality reduction and for ...
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INTRODUCTION. Semi-supervised learning has received a significant interest in pattern recognition and machine learning. While semi- supervised classification is ...
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data in this multi-manifold setting. We then ... own manifold in the feature space; in computer vision ... von Luxburg, U. (2007), 'A tutorial on spectral clustering',.by AB Goldberg · Cited by 151 · Related articles
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11.5 Active Learning and Semi-supervised Learning . . . . . . . . . . 36 ... several other methods, in particular for the case where labeled and unlabeled data.
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Our algorithm enables us to apply semi-supervised learning to a database of 80 million images gathered from the Internet. 1 Introduction. Gigantic quantities of ...by R Fergus · Cited by 272 · Related articles
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also introduce for any submodular function ... Define the cut function Γ(S) for a set of nodes S to be. Γ(S). ∑ ... A guide to the theory of NP-completeness. 1979.
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Apr 23, 2007 — Co-Training for Semi- supervised learning. (cont.) Machine Learning – 10701/15781. Carlos Guestrin. Carnegie Mellon University. April 23rd ...
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Mar 8, 2020 — Keywords: Semi-supervised learning, graph Laplacian, biconvex optimization, ... In this paper, we focus on a particular class of SSL methods, so called ... Note that many methods have built over this graph-based diffusion ...
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itory serve only as test data and are not used in the learning process. We present MISSL. (Multiple-Instance Semi-Supervised Learn- ing) that transforms any MI ...by R Rahmani · Cited by 154 · Related articles
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to introduce Cluster Kernels for semi-supervised learning. The authors suggest explicitly manipulating eigenvalues of the kernel matrix. 2 Regression on Graphs.by M Belkin · Cited by 657 · Related articles
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Then look for unlabeled examples where one rule is confident and the other is not. Have it label the example for the other. Training 2 classifiers, one on each type ...
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For the particular case of grasp learning, we also propose a combination of semi-supervised learning and active learning, another sub-field of machine learn-.by AN Erkan · 2010 · Cited by 34 · Related articles
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The rest of the paper is organized as follows. In Section 2 and Section 3, we introduce how to estimate the class condi-. Graph-Based Semi-Supervised Learning ...by J He · Cited by 49 · Related articles