The Experts below are selected from a list of 142959 Experts worldwide ranked by ideXlab platform
Zhi-hua Zhou - One of the best experts on this subject based on the ideXlab platform.
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Towards Safe Weakly Supervised Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Co-Authors: Lan-zhe Guo, Zhi-hua ZhouAbstract:In this paper, we study weakly Supervised Learning where a large amount of label information is not accessible. This includes incomplete supervision such as semi-Supervised Learning and domain adaptation; inexact supervision, such as multi-instance Learning and inaccurate supervision, such as label noise Learning. Unlike Supervised Learning, weakly Supervised Learning, however, may sometimes even degenerate performance. Such deficiency definitely hinders the deployment of weakly Supervised Learning to real applications. For this reason, it is desired to study safe weakly Supervised Learning. In this paper we present a generic ensemble Learning scheme to derive the safe prediction. We consider optimizing the worst-case performance gain which leads to a maximin optimization. Our resultant formulation brings multiple advantages. Firstly, for many commonly used convex loss functions in classification and regression tasks, our formulation is guaranteed to derive a safe prediction under a mild condition. Secondly, prior knowledge related to the weight of the base weakly Supervised learners can be flexibly embedded. Thirdly, our formulation can be globally and efficiently addressed. Finally, it is in an intuitive geometric interpretation. Extensive experiments on multiple weakly Supervised Learning tasks clearly demonstrate the effectiveness of our proposal algorithms.
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Disagreement-based Semi-Supervised Learning
Acta Automatica Sinica, 2013Co-Authors: Zhi-hua ZhouAbstract:Traditional Supervised Learning generally requires a large amount of labeled data as training examples; in many real tasks, however, although it is usually easy to acquire a lot of data, it is often expensive to get the label information.Can we improve the Learning performance with limited amount of labeled data by exploiting the large amount of unlabeled data? For this purpose, semi-Supervised Learning has become a hot topic of machine Learning during the past ten years.One of the mainstream paradigms, the disagreement-based semi-Supervised Learning, trains multiple learners to exploit the unlabeled data, where the "disagreement" among the learners is crucial. This article briefly surveys some research advances of this paradigm.
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Semi-Supervised Learning by disagreement
Knowledge and Information Systems, 2009Co-Authors: Zhi-hua ZhouAbstract:In many real-world tasks, there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and expertise. So, semi-Supervised Learning which tries to exploit unlabeled examples to improve Learning performance has become a hot topic. Disagreement-based semi-Supervised Learning is an interesting paradigm, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi-Supervised Learning process. This survey article provides an introduction to research advances in this paradigm.
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PAKDD - Budget Semi-Supervised Learning
Advances in Knowledge Discovery and Data Mining, 2009Co-Authors: Zhi-hua Zhou, Qiao-qiao She, Yuan JiangAbstract:In this paper we propose to study budget semi-Supervised Learning , i.e., semi-Supervised Learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the computational resource that can be used is generally not unlimited. Effective budget semi-Supervised Learning algorithms should be able to adjust behaviors considering the given resource budget. Roughly, the more resource, the more exploitation on unlabeled data. As an example, in this paper we show that this is achievable by a simple yet effective method.
Alexander Zien - One of the best experts on this subject based on the ideXlab platform.
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semi Supervised Learning
IEEE Transactions on Neural Networks, 2010Co-Authors: Olivier Chapelle, Bernhard Schlkopf, Alexander ZienAbstract:In the field of machine Learning, semi-Supervised Learning (SSL) occupies the middle ground, between Supervised Learning (in which all training examples are labeled) and unSupervised Learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step Learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-Supervised Learning and transduction. Adaptive Computation and Machine Learning series
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Semi-Supervised Learning - Semi-Supervised Learning
2006Co-Authors: Olivier Chapelle, Bernhard Schlkopf, Alexander ZienAbstract:In the field of machine Learning, semi-Supervised Learning (SSL) occupies the middle ground, between Supervised Learning (in which all training examples are labeled) and unSupervised Learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step Learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-Supervised Learning and transduction. Adaptive Computation and Machine Learning series
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Semi-Supervised Learning - A Discussion of Semi-Supervised Learning and Transduction
Semi-Supervised Learning, 2006Co-Authors: Olivier Chapelle, Bernhard Schölkopf, Alexander ZienAbstract:In the field of machine Learning, semi-Supervised Learning (SSL) occupies the middle ground, between Supervised Learning (in which all training examples are labeled) and unSupervised Learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step Learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-Supervised Learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard SchA¶lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in TA?bingen. SchA¶lkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational B iology (2004), all published by The MIT Press.
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Semi-Supervised Learning - Introduction to Semi-Supervised Learning
2006Co-Authors: Olivier Chapelle, Bernhard Schölkopf, Alexander ZienAbstract:This chapter contains sections titled: Supervised, UnSupervised, and Semi-Supervised Learning, When Can Semi-Supervised Learning Work?, Classes of Algorithms and Organization of This Book
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The Geometric Basis of Semi-Supervised Learning
2006Co-Authors: Olivier Chapelle, Bernhard Schölkopf, Alexander ZienAbstract:This chapter contains sections titled: Introduction, Incorporating Geometry in Regularization, Algorithms, Data-Dependent Kernels for Semi-Supervised Learning, Linear Methods for Large-Scale Semi-Supervised Learning, Connections to Other Algorithms and Related Work, Future Directions
Rich Caruana - One of the best experts on this subject based on the ideXlab platform.
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Semi-Supervised Learning with partially labeled examples
2010Co-Authors: Rich Caruana, Nam NguyenAbstract:Traditionally, machine Learning community has been focused on Supervised Learning where the source of Learning is fully labeled examples including both input features and corresponding output labels. As one way to alleviate the costly effort of collecting fully labeled examples, semi-Supervised Learning usually concentrates on utilizing a large amount of unlabeled examples together with a relatively small number of fully labeled examples to build better classifiers. Even though many semi-Supervised Learning algorithms are able to take advantage of unlabeled examples, there is a significant amount of effort in designing good models, features, kernels, and similarity functions. In this dissertation, we focus on semi-Supervised Learning with partially labeled examples. Partially labeled data can be viewed as a trade-off between fully labeled data and unlabeled data, which can provide additional discriminative information in comparison to unlabeled data and requires less human effort to collect than fully labeled data. In our setting of semi-Supervised Learning with partially labeled examples, the Learning method is provided with a large amount of partially labeled examples and is usually augmented with a relatively small set of fully labeled examples. Our main goal is to integrate partially labeled examples into the conventional Learning framework, i.e. to build a more accurate classifier. The dissertation addresses four different semi-Supervised Learning problems in presence of partially labeled examples. In addition, we summarize general principles for the semi-Supervised Learning with partially labeled examples.
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an empirical comparison of Supervised Learning algorithms
International Conference on Machine Learning, 2006Co-Authors: Rich Caruana, Alexandru NiculescumizilAbstract:A number of Supervised Learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of Supervised Learning was the Statlog Project in the early 90's. We present a large-scale empirical comparison between ten Supervised Learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based Learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the Learning methods.
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ICML - An empirical comparison of Supervised Learning algorithms
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006Co-Authors: Rich Caruana, Alexandru Niculescu-mizilAbstract:A number of Supervised Learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of Supervised Learning was the Statlog Project in the early 90's. We present a large-scale empirical comparison between ten Supervised Learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based Learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the Learning methods.
M. J. Healy - One of the best experts on this subject based on the ideXlab platform.
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A logical architecture for Supervised Learning
IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991Co-Authors: M. J. HealyAbstract:Summary form only given, as follows. The author discusses a neural network architecture for Supervised Learning with inherent stability properties. The architecture employs two ART 1 (adaptive resonance theory) unSupervised systems with supervision through interconnects. The Supervised Learning system, trained in a particular manner, responds properly to the training set of patterns and responds to novel inputs in a well-defined manner. A formal model characterizes the network in a system of logic. This system has potential applications in multisensor analysis, adaptive control, and neural network knowledge systems.
Alexandru Niculescumizil - One of the best experts on this subject based on the ideXlab platform.
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an empirical comparison of Supervised Learning algorithms
International Conference on Machine Learning, 2006Co-Authors: Rich Caruana, Alexandru NiculescumizilAbstract:A number of Supervised Learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of Supervised Learning was the Statlog Project in the early 90's. We present a large-scale empirical comparison between ten Supervised Learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based Learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the Learning methods.
