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Image retrieval: ideas, influences, and trends of the new age
- ACM COMPUTING SURVEYS
, 2008
"... We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger ass ..."
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Cited by 485 (13 self)
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We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
A review of feature selection techniques in bioinformatics
- BIOINFORMATICS
, 2007
"... Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed t ..."
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Cited by 358 (10 self)
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Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this paper, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
A Survey of Robot Learning from Demonstration
"... We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a ..."
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Cited by 281 (19 self)
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We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.
Editorial: special issue on learning from imbalanced data sets
- SIGKDD Explor. Newsl
, 2004
"... The class imbalance problem is one of the (relatively) new problems that emerged when machine learning matured from an embryonic science to an applied technology, amply used in the worlds of business, industry and scientific research. ..."
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Cited by 216 (5 self)
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The class imbalance problem is one of the (relatively) new problems that emerged when machine learning matured from an embryonic science to an applied technology, amply used in the worlds of business, industry and scientific research.
Efficient feature selection via analysis of relevance and redundancy
- Journal of Machine Learning Research
, 2004
"... Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature ..."
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Cited by 209 (3 self)
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Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature selection of high-dimensional data. We define feature redundancy and propose to perform explicit redundancy analysis in feature selection. A new framework is introduced that decouples relevance analysis and redundancy analysis. We develop a correlation-based method for relevance and redundancy analysis, and conduct an empirical study of its efficiency and effectiveness comparing with representative methods.
Fast Binary Feature Selection with Conditional Mutual Information
- Journal of Machine Learning Research
, 2004
"... We propose in this paper a very fast feature selection technique based on conditional mutual information. ..."
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Cited by 176 (1 self)
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We propose in this paper a very fast feature selection technique based on conditional mutual information.
Machine learning classifiers and fmri: A tutorial overview
- NeuroImage
, 2009
"... Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fM ..."
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Cited by 159 (6 self)
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Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fMRI data and thereby show the data contain enough information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of ‘is there information about a variable of interest ’ (pattern discrimination), classifiers can be used to tackle other classes of question, namely ‘where is the information ’ (pattern localization) and ‘how is that information encoded ’ (pattern characterization). 1
Sentiment analysis in multiple languages
- ACM Transactions on Information Systems
, 2008
"... The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of web forum opinions in multiple languages. The utility of stylistic and syntactic feat ..."
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Cited by 99 (6 self)
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The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The Entropy Weighted Genetic Algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of the key features. The proposed features and techniques are evaluated on a benchmark movie review data set and U.S. and Middle Eastern web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracy over 95 % on the benchmark data set and over 93 % for both the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all test beds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document level classification of sentiments.
Content-based image retrieval: approaches and trends of the new age
- In Proceedings ACM International Workshop on Multimedia Information Retrieval
, 2005
"... The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directio ..."
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Cited by 91 (3 self)
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The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation, spanning 120 references. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. We conclude with a study on the trends in volume and impact of publications in the field with respect to venues/journals and sub-topics.
Failure diagnosis using decision trees
- In Proceedings of the International Conference on Autonomic Computing (ICAC
, 2004
"... We present a decision tree learning approach to diagnosing failures in large Internet sites. We record runtime properties of each request and apply automated machine learning and data mining techniques to identify the causes of failures. We train decision trees on the request traces from time period ..."
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Cited by 89 (2 self)
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We present a decision tree learning approach to diagnosing failures in large Internet sites. We record runtime properties of each request and apply automated machine learning and data mining techniques to identify the causes of failures. We train decision trees on the request traces from time periods in which user-visible failures are present. Paths through the tree are ranked according to their degree of correlation with failure, and nodes are merged according to the observed partial order of system components. We evaluate this approach using actual failures from eBay, and find that, among hundreds of potential causes, the algorithm successfully identifies 13 out of 14 true causes of failure, along with 2 false positives. We discuss some results in applying simplified decision trees on eBay’s production site for several months. In addition, we give a cost-benefit analysis of manual vs. automated diagnosis systems. Our contributions include the statistical learning approach, the adaptation of decision trees to the context of failure diagnosis, and the deployment and evaluation of our tools on a high-volume production service. 1.
