Ethem Alpaydin | Bogazici University (original) (raw)
Papers by Ethem Alpaydin
International Journal of Pattern Recognition and Artificial Intelligence, 2009
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Abstract For building implementable and industryvaluable classification solutions, machine learni... more Abstract For building implementable and industryvaluable classification solutions, machine learning methods must focus not only on accuracy but also on computational and space complexity. We discuss a multistage method, namely cascading, where there is a sequence of classifiers ordered in terms of increasing complexity and specificity such that early classifiers are simple and general whereas later ones are more complex and specific, being localized on patterns rejected by the previous classifiers. We present the technique and its ...
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Abstract We discuss a multistage method, cascading, where there is a sequence of classifiers orde... more Abstract We discuss a multistage method, cascading, where there is a sequence of classifiers ordered in terms of complexity (of the classifier or the representation) and specificity, in that early classifiers are simple and general and later ones are more complex and are local. For building portable, low-cost handwriting recognizers, memory and computational requirements are as critical as accuracy and our proposed method, cascading, is a way to gain from having multiple classifiers, without much losing from cost. ...
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Pattern Recognition Letters, 1994
Abstract The comparative performances of distributed and local neural networks for the speech rec... more Abstract The comparative performances of distributed and local neural networks for the speech recognition problem are investigated. We consider a feed-forward network with one or more hidden layers. Depending on the response characteristics of the hidden units, we name the network distributed or local. If the hidden units use the sigmoid non-linearity, then hidden units have a global response and we call such networks distributed. If each hidden unit responds only to inputs in a certain local region in the input space, then the network is ...
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IEEE Transactions on Neural Networks, 2001
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Neural Networks, 1998
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Abstract Learners based on different paradigms can be combined for improved accuracy. Each learni... more Abstract Learners based on different paradigms can be combined for improved accuracy. Each learning method assumes a certain model that comes with a set of assumptions which may lead to error if the assumptions do not hold. Learning is an ill-posed problem and with finite data each algorithm converges to a different solution and fails under different circumstances. Our previous experience with statistical and neural classifiers was that classifiers based on these paradigms do generalize differently, fail on different patterns ...
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IEEE Transactions on Neural Networks, 2002
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IEEE Transactions on Neural Networks, 2002
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Pen-based handwriting recognition has enormous practical utility. It isdifferent from optical rec... more Pen-based handwriting recognition has enormous practical utility. It isdifferent from optical recognition in that the input is a temporal signal of pen movementsas opposed to a static spatial pattern. We examine various ways of combining multiplelearners which are trained with different representations of the same input signal: dynamic (pen movements) and static (final 2D image). We notice that the classifiers based ondifferent representations fail for different patterns and investigate ways to combine thetwo representations. We benchmark ...
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Completely parallel object recognition is NP-complete. Achievinga recognizer with feasible comple... more Completely parallel object recognition is NP-complete. Achievinga recognizer with feasible complexity requires a compromise betweenparallel and sequential processing where a system selectivelyfocuses on parts of a given image, one after another. Successivefixations are generated to sample the image and these samples areprocessed and abstracted to generate a temporal context in whichresults are integrated over time. A computational model based on apartially recurrent feedforward network is proposed and made credibleby ...
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Artificial Intelligence Review, 1997
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IEEE Transactions on Neural Networks, 1996
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Neural Computation, 1999
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Support vector machines (SVMs) are primarily designed for 2-class classification problems. Althou... more Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can be used to solve a K-class classification problem, such a procedure requires some care. In this paper, the scaling problem of different SVMs is highlighted. Various normalization methods are proposed to cope with this problem and their efficiencies are measured empirically. This simple way of ssing SVMs to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K SVMs solving a one-per-class decomposition of the general problem. In the second part of this paper, more sophisticated techniques are suggested. On the one hand, a stacking of the K SVMs with other classification techniques is proposed. On the other end, the one-per-class decomposition scheme is replaced by more elaborated schemes based on error-correcting codes. An incremental algorithm for the elaboration of pertinent decomposition schemes is mentioned, which exploits the properties of SVMs for an efficient computation.
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The goal of machine learning is to program computers to use example data or past experience to so... more The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of ...
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International Journal of Pattern Recognition and Artificial Intelligence, 2009
Bookmarks Related papers MentionsView impact
Abstract For building implementable and industryvaluable classification solutions, machine learni... more Abstract For building implementable and industryvaluable classification solutions, machine learning methods must focus not only on accuracy but also on computational and space complexity. We discuss a multistage method, namely cascading, where there is a sequence of classifiers ordered in terms of increasing complexity and specificity such that early classifiers are simple and general whereas later ones are more complex and specific, being localized on patterns rejected by the previous classifiers. We present the technique and its ...
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Abstract We discuss a multistage method, cascading, where there is a sequence of classifiers orde... more Abstract We discuss a multistage method, cascading, where there is a sequence of classifiers ordered in terms of complexity (of the classifier or the representation) and specificity, in that early classifiers are simple and general and later ones are more complex and are local. For building portable, low-cost handwriting recognizers, memory and computational requirements are as critical as accuracy and our proposed method, cascading, is a way to gain from having multiple classifiers, without much losing from cost. ...
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Pattern Recognition Letters, 1994
Abstract The comparative performances of distributed and local neural networks for the speech rec... more Abstract The comparative performances of distributed and local neural networks for the speech recognition problem are investigated. We consider a feed-forward network with one or more hidden layers. Depending on the response characteristics of the hidden units, we name the network distributed or local. If the hidden units use the sigmoid non-linearity, then hidden units have a global response and we call such networks distributed. If each hidden unit responds only to inputs in a certain local region in the input space, then the network is ...
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Bookmarks Related papers MentionsView impact
IEEE Transactions on Neural Networks, 2001
Bookmarks Related papers MentionsView impact
Neural Networks, 1998
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Abstract Learners based on different paradigms can be combined for improved accuracy. Each learni... more Abstract Learners based on different paradigms can be combined for improved accuracy. Each learning method assumes a certain model that comes with a set of assumptions which may lead to error if the assumptions do not hold. Learning is an ill-posed problem and with finite data each algorithm converges to a different solution and fails under different circumstances. Our previous experience with statistical and neural classifiers was that classifiers based on these paradigms do generalize differently, fail on different patterns ...
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IEEE Transactions on Neural Networks, 2002
Bookmarks Related papers MentionsView impact
IEEE Transactions on Neural Networks, 2002
Bookmarks Related papers MentionsView impact
Pen-based handwriting recognition has enormous practical utility. It isdifferent from optical rec... more Pen-based handwriting recognition has enormous practical utility. It isdifferent from optical recognition in that the input is a temporal signal of pen movementsas opposed to a static spatial pattern. We examine various ways of combining multiplelearners which are trained with different representations of the same input signal: dynamic (pen movements) and static (final 2D image). We notice that the classifiers based ondifferent representations fail for different patterns and investigate ways to combine thetwo representations. We benchmark ...
Bookmarks Related papers MentionsView impact
Completely parallel object recognition is NP-complete. Achievinga recognizer with feasible comple... more Completely parallel object recognition is NP-complete. Achievinga recognizer with feasible complexity requires a compromise betweenparallel and sequential processing where a system selectivelyfocuses on parts of a given image, one after another. Successivefixations are generated to sample the image and these samples areprocessed and abstracted to generate a temporal context in whichresults are integrated over time. A computational model based on apartially recurrent feedforward network is proposed and made credibleby ...
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Artificial Intelligence Review, 1997
Bookmarks Related papers MentionsView impact
IEEE Transactions on Neural Networks, 1996
Bookmarks Related papers MentionsView impact
Neural Computation, 1999
Bookmarks Related papers MentionsView impact
Support vector machines (SVMs) are primarily designed for 2-class classification problems. Althou... more Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can be used to solve a K-class classification problem, such a procedure requires some care. In this paper, the scaling problem of different SVMs is highlighted. Various normalization methods are proposed to cope with this problem and their efficiencies are measured empirically. This simple way of ssing SVMs to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K SVMs solving a one-per-class decomposition of the general problem. In the second part of this paper, more sophisticated techniques are suggested. On the one hand, a stacking of the K SVMs with other classification techniques is proposed. On the other end, the one-per-class decomposition scheme is replaced by more elaborated schemes based on error-correcting codes. An incremental algorithm for the elaboration of pertinent decomposition schemes is mentioned, which exploits the properties of SVMs for an efficient computation.
Bookmarks Related papers MentionsView impact
The goal of machine learning is to program computers to use example data or past experience to so... more The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of ...
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