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• Shallow Parsing by Inferencing with Classifiers.
• We formalize it as the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints, and develop two general approaches for it. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observations structure and of general classifiers to model state-observation dependencies. ...
• Vasin Punyakanok and Dan Roth, Shallow Parsing by Inferencing with Classifiers. ...

• Probabilistic Combination of Text Classifiers Using Reliability Indicators: Models and Results.
• The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifiers. We introduce a probabilistic method for combining classifiers that considers the context-sensitive reliabilities of contributing classifiers. The method harnesses reliability indicators --variables that provide a valuable signal about the performance of classifiers in different situations. ...

• Introduction to Classifiers.
• This workshop will provide the participants with an introduction to the basic use of classifiers: the different handshapes and sizes, how they represent people, things and action.   Participants will be able to "draw" or "see" pictures in addition to using classifiers in sentences. ...
• Can Read/Convey Classifiers .

• Up: META-LEARNING Previous: Computing Initial Base Classifiers. ...
• Integrating Base Classifiers.
• How precisely do we integrate a number of separately learned classifiers? Bayesian statistics theory provides one possible approach to combining several learned classifiers based upon the statistics of the behavior of the classifiers on the training set. Given some set of classifiers, and a feature vector x, we seek to compute a class label y for x. ...
• ) (Furthermore, Bayes theorem would be optimal if we knew all possible classifiers, not just those that we happen to compute. ) This information, however, provides only statistics about each classifier's behavior with respect to the training set, and no information about how the classifiers are related to each other. For example, learning that two classifiers rarely agree with each other when predicting a class label y (meaning that when one classifier predicts y, the other does not) might have much more predictive value (eg. when combined with a third classifier) than merely knowing that the two classifiers predict y with equal probability! We view the purely Bayesian approach as a baseline, and use methods derived from this approach, BAYES and Bayesian-belief for comparative purposes in our experiments. There are many other approaches we might imagine that are based upon learning relationships between classifiers. The manner in which we learn the relationship between classifiers is to learn a new classifier (a ``meta-level classifier'') whose input is the set of predictions of two or more classifiers on common data. ...
• An arbiter is learned by some learning algorithm to arbitrate among predictions generated by different base classifiers. ... A final prediction is made with predictions input from two base classifiers and a single arbiter. ...
• In the combiner strategy, the predictions of the learned base classifiers on the training set form the basis of the meta-learner's training set. ... In classifying an instance, the base classifiers first generate their predictions. ... The aim of this strategy is to ``correlate'' the predictions from the base classifiers by learning the relationship between these predictions and the correct prediction. A combiner computes a prediction that may be entirely different from any proposed by a base classifier, whereas an arbiter chooses one of the predictions from the base classifiers and the arbiter itself. ...

• Learning Naïve Bayes Classifiers From Attribute Value Taxonomies and Partially Specified Data.
• Zhang, Jun and Honavar, Vasant (2004) Learning Naïve Bayes Classifiers From Attribute Value Taxonomies and Partially Specified Data. ...
• This paper describes AVT-NBL -- a variant of the Naïve Bayes Learning algorithm that effectively exploits user-supplied attribute value taxonomies to construct compact and accurate Naïve Bayes classifiers from partially specified data. Our experiments with several data sets and AVTs show that AVT-NBL yields classifiers that are substantially more accurate and more compact than those obtained using the standard Naïve Bayes learner.

• 595 Learning Bayesian Network Classifiers for Facial Expression Recognition using both Labeled and Unlabeled Data .
• We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. ... We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. ... We also provide results using the Naive Bayes (NB) and the Tree-Augmented Naive Bayes (TAN) classifiers, showing that the two can achieve good performance with labeled training sets, but perform poorly when unlabeled data are added to the training set. ...

• Illinois Soil Classifiers Association.
• The Illinois Soil Classifiers Association is an organization promoting the wise use of the soil resource. ISCA is made up of professional soil classifiers in public service, private industry, and education and includes students and others interested in preserving soil. ...

• Realisable Classifiers: Improving Operating Performance on Variable Cost Problems .
• By analysis of a set of existing classifiers using a receiver operating characteristic (ROC) curve, a set of new realisable classifiers may be obtained by a random combination of two of the existing classifiers. These classifiers lie on the convex hull that contains the original ROC points for the existing classifiers. ... A theorem for this method is derived and proved from an observation about ROC data, and experimental results verify that a superior classification system may be constructed using only the existing classifiers and the information of the original ROC data. ...

• Bayesian Network Classifiers .
• Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4. ... In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. ...

• Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments.
• Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. ... First, we provide numerous results giving insight into the hardness of the simplicity-accuracy tradeoff for voting classifiers. ... It is to our knowledge the first attempt to build a voting classifier as a base formula using the weak learning framework (the one which was previously highly successful for decision tree induction), and not the strong learning framework (as usual for such classifiers with boosting-like approaches). ...

• Construction of Classifiers.
• Construction of classifiers is a fundamental and primary topic in pattern recognition. From the beginning of the study of pattern recognition, many construction methods of classifiers have been proposed. For example, some classical and recently developed classifiers are listed below: .
• Classical Classifiers .
• Recently Developed Classifiers .
• PL (Piecewise Linear) Classifiers .
• NL (Nonlinear) Classifiers .
• MM (Mixture Model) Classifiers .
• In this document, the construction methods of the classifiers are briefly introduced. The classical classifiers are the first, and some recently developed classifiers follow. ...
• Classical Classifiers.
• Traditionally, the construction methods of classifiers are based on approximating the class-conditional probabilistic density functions (PDFs). ...
• Recently Developed Classifiers.
• The classical classifiers are simple and sufficient for easy problems. ...
• Practical classifiers have to overcome such difficulties. ... On the other hand, some nonlinear classifiers also succeeded by generating a new higher dimensional feature space from the original features, and by constructing a simple classifier in the new feature space. ...

• Hibryd and Multi-layered Classifiers in Medicine .
• Contributions are solicited for a special issue of the Artificial Intelligence in Medicine journal on the theme of "Hybrid and Multi Classifiers in Medicine". ...
• Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. ...
• There are many methods for combining the predictions given by component classifiers. ...
• During the past several years, in a variety of application domains, researchers in machine learning, computational learning theory, pattern recognition and statistics have re-ignited the effort to learn how to create and combine an ensemble of classifiers. This research has the potential to apply accurate composite classifiers to real world problems by intelligently combining known learning algorithms.
• Combining the predictions of a set of component classifiers has shown to yield accuracy higher than the most accurate component on a long variety of supervised classification problems.
• Accepted papers will appear in the special issue of the journal Artificial Intelligence in Medicine on Hybrid and Multi-Layered Classifiers in Medicine. ...

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