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
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
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
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.
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
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
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
In this chapter, we present an algorithmic framework for semi-supervised inference based on geometric properties of probability distributions. Our approach ...
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
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 ...
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
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
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
1 Introduction. Topic modeling based on Latent Dirichlet Allocation (LDA)  has become a popu- lar tool for data exploration, dimensionality reduction and for ...
INTRODUCTION. Semi-supervised learning has received a significant interest in pattern recognition and machine learning. While semi- supervised classification is ...
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
11.5 Active Learning and Semi-supervised Learning . . . . . . . . . . 36 ... several other methods, in particular for the case where labeled and unlabeled data.
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
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.
Apr 23, 2007 — Co-Training for Semi- supervised learning. (cont.) Machine Learning – 10701/15781. Carlos Guestrin. Carnegie Mellon University. April 23rd ...
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 ...
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
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
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 ...
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
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