Ynthesis Lectures On Artificial Intelligence And Machine Learning Pdf

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De Raedt , K. Kersting , S.

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns.

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Synthesis Lectures on Artificial Intelligence and Machine Learning

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Introduction to Semi-Supervised Learning.

Cainan Teixeira. Download PDF. A short summary of this paper. Brachman, Yahoo! No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.

Goldberg www. 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 is 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. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi- supervised learning, and we conclude the book with a brief discussion of open questions in the field.

Transductive Semi-Supervised Learning. It is aimed at advanced under- graduates, entry-level graduate students and researchers in areas as diverse as Computer Science, Electrical Engineering, Statistics, and Psychology. The book assumes that the reader is familiar with elementary calculus, probability and linear algebra.

It is helpful, but not necessary, for the reader to be familiar with statistical machine learning, as we will explain the essential concepts in order for this book to be self-contained. Sections containing more advanced materials are marked with a star.

We also provide a basic mathematical reference in Appendix A. Our focus is on semi-supervised model assumptions and computational techniques. We inten- tionally avoid competition-style benchmark evaluations. This is because, in general, semi-supervised learning models are sensitive to various settings, and no benchmark that we know of can characterize the full potential of a given model on all tasks.

Such analysis is not frequently encountered in the literature. Semi-supervised learning has grown into a large research area within machine learning.

While we attempt to provide a basic coverage of semi-supervised learning, the se- lected topics are not able to reflect the most recent advances in the field. We would like to express our sincere thanks to Thorsten Joachims and the other reviewers for their constructive reviews that greatly improved the book. We thank Robert Nowak for his excellent learning theory lecture notes, from which we take some materials for Section 8.

We hope you enjoy the book. Xiaojin Zhu and Andrew B. Readers familiar with machine learning may wish to skip directly to Section 2, where we introduce semi-supervised learning. Example 1. You arrive at an extrasolar planet and are welcomed by its resident little green men. You observe the weight and height of little green men around you, and plot the measurements in Figure 1. What can you learn from this data? Figure 1. Each green dot is an instance, represented by two features: weight and height.

This is a typical example of a machine learning scenario except the little green men part. Before exploring such machine learning tasks, let us begin with some definitions. An instance x represents a specific object. The length D of the feature vector is known as the dimensionality of the feature vector.

The feature representation is an abstraction of the objects. It essentially ignores all other infor- mation not represented by the features. For example, two little green men with the same weight and height, but with different names, will be regarded as indistinguishable by our feature representation.

Note we use boldface x to denote the whole instance, and xd to denote the d-th feature of x. Features can also take discrete values. Definition 1. Training Sample. We assume these instances are sampled independently from an underlying distribution P x , which is unknown to us. We denote this by i. What the algorithm can learn from it, however, varies. In this chapter, we introduce two basic learning paradigms: unsupervised learning and supervised learning.

Unsupervised learning. There is no teacher providing supervision as to how individual instances should be handled—this is the defining property of unsupervised learning. Among the unsupervised learning tasks, the one most relevant to this book is clustering, which we discuss in more detail. The number of clusters k may be specified by the user, or may be inferred from the training sample itself.

Without further assumptions, either one is acceptable. Unlike in supervised learning introduced in the next section , there is no teacher that tells us which instances should be in each cluster. There are many clustering algorithms. We introduce a particularly simple one, hierarchical agglomerative clustering, to make unsupervised learning concrete.

Algorithm 1. Hierarchical Agglomerative Clustering. Initially, place each instance in its own cluster called a singleton cluster. Find the closest cluster pair A, B, i. Merge A, B to form a new cluster. Output: a binary tree showing how clusters are gradually merged from singletons to a root cluster, which contains the whole training sample. This clustering algorithm is simple. The only thing unspecified is the distance function d.

There are multiple possibilities: one can define it to be the distance between the closest pair of points in A and B, the distance between the farthest pair, or some average distance. The clusters certainly look fine. But because there is no information on how each instance should be clustered, it can be difficult to objectively evaluate the result of clustering algorithms. Alternatively, you may want to predict whether an alien is a juvenile or an adult using weight and height.

To explain how to approach these tasks, we need more definitions. A label y is the desired prediction on an instance x. Labels may come from a finite set of values, e.

These distinct values are called classes. The classes are usually encoded by integer numbers, e. This particular encoding is often used for binary two-class labels, and the two classes are generically called the negative class and the positive class, respectively. In general, such encoding does not imply structure in the classes. Labels may also take continuous values in R. For example, one may attempt to predict the blood pressure of little green aliens based on their height and weight.

One can think of y as the label on x provided by a teacher, hence the name supervised learning. Such instance, label pairs are called labeled data, while instances alone without labels as in unsupervised learning are called unlabeled data. We are now ready to define supervised learning. Supervised learning. Let the domain of instances be X , and the domain of labels be Y. Classification is the supervised learning problem with discrete classes Y.

The function f is called a classifier. Regression is the supervised learning problem with continuous Y. The function f is called a regression function.

Introduction to Semi-Supervised Learning

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Synthesis Lectures on Artificial Intelligence and Machine Learning | Read 0 articles with Chapter 1 is a short introduction to the field of multiagent systems.


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Diederik M. Libin, Jan Helsen, Diederik M. Nature: Scientific Reports vol. Knowledge Engineering Review , Special issue on New Horizons in Multiagent Learning.

Diego Kozlowski: Machine Learning on graphs

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies.

Skip to Main Content. How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws.

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