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310
Active Learning to Recognize Multiple Types of Plankton
 Journal of Machine Learning Research
, 2004
"... Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The ..."
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Cited by 45 (5 self)
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Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The experimental results from a plankton recognition system indicate that our approach often requires significantly less labeled images to maintain the same accuracy level as random sampling. 1.
Large Scale MaxMargin MultiLabel Classification with Priors
"... We propose a maxmargin formulation for the multilabel classification problem where the goal is to tag a data point with a set of prespecified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this ..."
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Cited by 40 (2 self)
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We propose a maxmargin formulation for the multilabel classification problem where the goal is to tag a data point with a set of prespecified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Existing solutions take either of two approaches. The first assumes, a priori, that there are no label correlations and independently trains a classifier for each label (as is done in the 1vsAll heuristic). This reduces the problem complexity from exponential to linear and such methods can scale to large problems. The second approach explicitly models correlations by pairwise label interactions. However, the complexity remains exponential unless one assumes that label correlations are sparse. Furthermore, the learnt correlations reflect the training set biases. We take a middle approach that assumes labels are correlated but does not incorporate pairwise label terms in the prediction function. We show that the complexity can still be reduced from exponential to linear while modelling dense pairwise label correlations. By incorporating correlation priors we can overcome training set biases and improve prediction accuracy. We provide a principled interpretation of the 1vsAll method and show
A Sequential Dual Method for Large Scale MultiClass Linear SVMs
, 2008
"... Efficient training of direct multiclass formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as well as features. This paper presents a fast dual method for this training. The main idea is to sequentially traverse thro ..."
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Cited by 37 (8 self)
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Efficient training of direct multiclass formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as well as features. This paper presents a fast dual method for this training. The main idea is to sequentially traverse through the training set and optimize the dual variables associated with one example at a time. The speed of training is enhanced further by shrinking and cooling heuristics. Experiments indicate that our method is much faster than state of the art solvers such as bundle, cutting plane and exponentiated gradient methods.
Mining Statistically Important Equivalence Classes and DeltaDiscriminative Emerging Patterns
, 2007
"... The supportconfidence framework is the most common measure used in itemset mining algorithms, for its antimonotonicity that effectively simplifies the search lattice. This computational convenience brings both quality and statistical flaws to the results as observed by many previous studies. In thi ..."
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Cited by 35 (2 self)
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The supportconfidence framework is the most common measure used in itemset mining algorithms, for its antimonotonicity that effectively simplifies the search lattice. This computational convenience brings both quality and statistical flaws to the results as observed by many previous studies. In this paper, we introduce a novel algorithm that produces itemsets with ranked statistical merits under sophisticated test statistics such as chisquare, risk ratio, odds ratio, etc. Our algorithm is based on the concept of equivalence classes. An equivalence class is a set of frequent itemsets that always occur together in the same set of transactions. Therefore, itemsets within an equivalence class all share the same level of statistical significance regardless of the variety of test statistics. As an equivalence class can be uniquely determined and concisely represented by a closed pattern and a set of generators, we just mine closed patterns and generators, taking a simultaneous depthfirst search scheme. This parallel approach has not been exploited by any prior work. We evaluate our algorithm on two aspects. In general, we compare to LCM and FPclose which are the best algorithms tailored for mining only closed patterns. In particular, we compare to epMiner which is the most recent algorithm for mining a type of relative risk patterns, known as minimal emerging patterns. Experimental results show that our algorithm is faster than all of them, sometimes even multiple orders of magnitude faster. These statistically ranked patterns and the efficiency have a high potential for reallife applications, especially in biomedical and financial fields where classical test statistics are of dominant interest.
Large Scale MultiLabel Classification via MetaLabeler
 WWW 2009 MADRID! TRACK: DATA MINING / SESSION: LEARNING
, 2009
"... The explosion of online content has made the management of such content nontrivial. Webrelated tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multilabel categorization systems on a large scale. Existing multil ..."
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Cited by 34 (7 self)
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The explosion of online content has made the management of such content nontrivial. Webrelated tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multilabel categorization systems on a large scale. Existing multilabel classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive crossvalidation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multilabeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.
Recent Advances of Largescale Linear Classification
"... Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much ..."
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Cited by 33 (6 self)
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Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much faster. Recently, many research works have developed efficient optimization methods to construct linear classifiers and applied them to some largescale applications. In this paper, we give a comprehensive survey on the recent development of this active research area.
Learning from partial labels
 Journal of Machine Learning Research
, 2011
"... We address the problem of partiallylabeled multiclass classification, where instead of a single label per instance, the algorithm is given a candidate set of labels, only one of which is correct. Our setting is motivated by a common scenario in many image and video collections, where only partial a ..."
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Cited by 31 (0 self)
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We address the problem of partiallylabeled multiclass classification, where instead of a single label per instance, the algorithm is given a candidate set of labels, only one of which is correct. Our setting is motivated by a common scenario in many image and video collections, where only partial access to labels is available. The goal is to learn a classifier that can disambiguate the partiallylabeled training instances, and generalize to unseen data. We define an intuitive property of the data distribution that sharply characterizes the ability to learn in this setting and show that effective learning is possible even when all the data is only partially labeled. Exploiting this property of the data, we propose a convex learning formulation based on minimization of a loss function appropriate for the partial label setting. We analyze the conditions under which our loss function is asymptotically consistent, as well as its generalization and transductive performance. We apply our framework to identifying faces culled from web news sources and to naming characters in TV series and movies; in particular, we annotated and experimented on a very large video data set and achieve 6 % error for character
Ensembles of nested dichotomies for multiclass problems
 In Proc 21st International Conference on Machine Learning
, 2004
"... Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multiclass classification task into a system of dichotomies and provide a statistically sound wa ..."
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Cited by 31 (5 self)
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Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multiclass classification task into a system of dichotomies and provide a statistically sound way of applying twoclass learning algorithms to multiclass problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multiclass problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to errorcorrecting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good generalpurpose method for applying binary classifiers to multiclass problems.
A userâ€™s guide to support vector machines
, 2007
"... The Support Vector Machine (SVM) is a widely used classifier. And yet, obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. We provide the user with a basic understanding of the theory behind SVMs and focus on thei ..."
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Cited by 30 (0 self)
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The Support Vector Machine (SVM) is a widely used classifier. And yet, obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. We provide the user with a basic understanding of the theory behind SVMs and focus on their use in practice. We describe the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs. 1