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Ryan Rifkin Thesis

Everything Old Is New Again: A Fresh Look at ... - Poggio Lab - MIT
Ryan Michael Rifkin. Submitted ... This thesis shows that several old, somewhat discredited machine learning techniques ... Thesis Supervisor: Tomaso Poggio.

Ryan Rifkin Thesis

The choice of loss function determines the learning scheme. Although a large body of recent literature exists suggesting the use of sophisticated schemes involving joint optimization of multiple discriminant functions or the use of error-correcting codes, we instead advocate a simple one-vs-all approach in which one classifier is trained to discriminate each class from all the others, and a new point is classified according to which classifier fires most strongly. While it is widely believed that the svm will perform substantially better than rlsc, we note that the same generalization bounds that apply to svms apply to rlsc, and we demonstrate empirically on both toy and real-world examples that rlscs performance is essentially equivalent to svms across a wide range of problems, implying that the choice between svm and rlsc should be based on computational tractability considerations.

Rlsc is trained by solving a single system of linear equations. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments. We also consider, and advocate in many cases, the use of the more classical square loss, giving rise to the regularized least squares classifiation algorithm.

Some features of this site may not work without it. We also present leave-one-out bounds for multiclass classification where the base learners belong to a broad class of tikhonov regularizers. We consider the problem of multiclass classification.

All items in dspacemit are protected by original copyright, with all rights reserved, unless otherwise indicated. Next, we turn to the problem of multiclass classification. Institute of technology, sloan school of management, 2002.

Finally, we consider algorithmic stability, a relatively new theory that results in very elegant generalization bounds for algorithms which are stable. We begin by considering tikhonov regularization, a broad framework of schemes for binary classification. We compare and contrast tikhonov regularization, to which algorithmic stability applies, with ivanov regularization, the form of regularization that is the basis for structural risk minimization and its related generalization bounds.

This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. We also prove leave-one-out bounds for rlsc classification. We demonstrate the empirical advantages and properties of rlsc, discussing the tradeoffs between rlsc and svms. We present empirical evidence of the strength of this scheme on real-world problems, and, in the context of rlsc as the base classifier, compelling arguments as to why the simple one-vs-all scheme is hard to beat. We discuss the design and implementation issues involved in svmfu, present empirical results on its performance, and offer general guidance on the use of svms to solve machine learning problems.


Everything old is new again: a fresh look at historical approaches in ...


Author: Rifkin, Ryan Michael, 1972- ... This thesis shows that several old, somewhat discredited machine learning techniques are still valuable in the solution of ...

Ryan Rifkin Thesis

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Ryan Rifkin Thesis 18 Nov 2018. The Static Stochastic Ground-Holding Problem. Thesis Supervisor: Tomaso Poggio. Using the hinge loss gives rise to the now well-known support vector machine algorithm. We demonstrate the empirical advantages and properties of rlsc, discussing the tradeoffs between rlsc and svms. This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. Our main thesis is that a simple "one-vs-all" scheme is as accurate as any other approach, assuming , We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work.
  • Ryan Rifkin - Chessprogramming wiki


    We consider the problem of multiclass classification. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments. This thesis shows that several old, somewhat discredited machine learning techniques are still valuable in the solution of modern, large-scale machine learning problems. We begin by considering tikhonov regularization, a broad framework of schemes for binary classification. We present some interesting examples which highlight the differences between the tikhonov and ivanov approaches, showing that the tikhonov form has a much stronger stability than the ivanov form.

    We present svmfu, a state-of-the-art svm solver developed as part of thesis. Although a large body of recent literature exists suggesting the use of sophisticated schemes involving joint optimization of multiple discriminant functions or the use of error-correcting codes, we instead advocate a simple one-vs-all approach in which one classifier is trained to discriminate each class from all the others, and a new point is classified according to which classifier fires most strongly. We also prove leave-one-out bounds for rlsc classification. We demonstrate the empirical advantages and properties of rlsc, discussing the tradeoffs between rlsc and svms. We discuss the design and implementation issues involved in svmfu, present empirical results on its performance, and offer general guidance on the use of svms to solve machine learning problems.

    Our main thesis is that a simple one-vs-all scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. Next, we turn to the problem of multiclass classification. Tikhonov regularization attempts to find a function which simultaneously has small empirical loss on a training set and small norm in a reproducing kernel hilbert space. We also consider, and advocate in many cases, the use of the more classical square loss, giving rise to the regularized least squares classifiation algorithm. We compare and contrast tikhonov regularization, to which algorithmic stability applies, with ivanov regularization, the form of regularization that is the basis for structural risk minimization and its related generalization bounds. Finally, we consider algorithmic stability, a relatively new theory that results in very elegant generalization bounds for algorithms which are stable. We also present leave-one-out bounds for multiclass classification where the base learners belong to a broad class of tikhonov regularizers. While it is widely believed that the svm will perform substantially better than rlsc, we note that the same generalization bounds that apply to svms apply to rlsc, and we demonstrate empirically on both toy and real-world examples that rlscs performance is essentially equivalent to svms across a wide range of problems, implying that the choice between svm and rlsc should be based on computational tractability considerations. Institute of technology, sloan school of management, 2002. .

    18 Nov 2018 ... A.I. Memo 1649, MIT Artificial Intelligence Lab, CiteSeerX; Ryan Rifkin (1998). The Static Stochastic Ground-Holding Problem. Master's thesis ...

    LEVERAGING PRIVACY IN DATA ANALYSIS Ryan ... - Penn Math

    Ryan Michael Rogers ..... We then conclude this first part of the thesis with empirical evaluations ...... Paper 955. S. Mukherjee, P. Niyogi, T. Poggio, and R. Rifkin.
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    This thesis shows that several old, somewhat discredited machine learning techniques are still valuable in the solution of modern, large-scale machine learning problems. We demonstrate the empirical advantages and properties of rlsc, discussing the tradeoffs between rlsc and svms. Some features of this site may not work without it. We consider the problem of multiclass classification. All items in dspacemit are protected by original copyright, with all rights reserved, unless otherwise indicated.

    Although a large body of recent literature exists suggesting the use of sophisticated schemes involving joint optimization of multiple discriminant functions or the use of error-correcting codes, we instead advocate a simple one-vs-all approach in which one classifier is trained to discriminate each class from all the others, and a new point is classified according to which classifier fires most strongly Buy now Ryan Rifkin Thesis

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    We present empirical evidence of the strength of this scheme on real-world problems, and, in the context of rlsc as the base classifier, compelling arguments as to why the simple one-vs-all scheme is hard to beat. We also consider, and advocate in many cases, the use of the more classical square loss, giving rise to the regularized least squares classifiation algorithm. All items in dspacemit are protected by original copyright, with all rights reserved, unless otherwise indicated. We begin by considering tikhonov regularization, a broad framework of schemes for binary classification. We also prove leave-one-out bounds for rlsc classification.

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    While it is widely believed that the svm will perform substantially better than rlsc, we note that the same generalization bounds that apply to svms apply to rlsc, and we demonstrate empirically on both toy and real-world examples that rlscs performance is essentially equivalent to svms across a wide range of problems, implying that the choice between svm and rlsc should be based on computational tractability considerations. Our main thesis is that a simple one-vs-all scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. Next, we turn to the problem of multiclass classification. We also present leave-one-out bounds for multiclass classification where the base learners belong to a broad class of tikhonov regularizers Buy Ryan Rifkin Thesis at a discount

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    We also consider, and advocate in many cases, the use of the more classical square loss, giving rise to the regularized least squares classifiation algorithm. Using the hinge loss gives rise to the now well-known support vector machine algorithm. Our main thesis is that a simple one-vs-all scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. We discuss the design and implementation issues involved in svmfu, present empirical results on its performance, and offer general guidance on the use of svms to solve machine learning problems. We also present leave-one-out bounds for multiclass classification where the base learners belong to a broad class of tikhonov regularizers Buy Online Ryan Rifkin Thesis

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    We present svmfu, a state-of-the-art svm solver developed as part of thesis. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments. The choice of loss function determines the learning scheme. Although a large body of recent literature exists suggesting the use of sophisticated schemes involving joint optimization of multiple discriminant functions or the use of error-correcting codes, we instead advocate a simple one-vs-all approach in which one classifier is trained to discriminate each class from all the others, and a new point is classified according to which classifier fires most strongly Buy Ryan Rifkin Thesis Online at a discount

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    We demonstrate the empirical advantages and properties of rlsc, discussing the tradeoffs between rlsc and svms. We begin by considering tikhonov regularization, a broad framework of schemes for binary classification. Institute of technology, sloan school of management, 2002. We present empirical evidence of the strength of this scheme on real-world problems, and, in the context of rlsc as the base classifier, compelling arguments as to why the simple one-vs-all scheme is hard to beat. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments.

    Although a large body of recent literature exists suggesting the use of sophisticated schemes involving joint optimization of multiple discriminant functions or the use of error-correcting codes, we instead advocate a simple one-vs-all approach in which one classifier is trained to discriminate each class from all the others, and a new point is classified according to which classifier fires most strongly Ryan Rifkin Thesis For Sale

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    We also consider, and advocate in many cases, the use of the more classical square loss, giving rise to the regularized least squares classifiation algorithm. We compare and contrast tikhonov regularization, to which algorithmic stability applies, with ivanov regularization, the form of regularization that is the basis for structural risk minimization and its related generalization bounds. Tikhonov regularization attempts to find a function which simultaneously has small empirical loss on a training set and small norm in a reproducing kernel hilbert space. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments For Sale Ryan Rifkin Thesis

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    We demonstrate the empirical advantages and properties of rlsc, discussing the tradeoffs between rlsc and svms. The choice of loss function determines the learning scheme. Our main thesis is that a simple one-vs-all scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. All items in dspacemit are protected by original copyright, with all rights reserved, unless otherwise indicated. Next, we turn to the problem of multiclass classification.

    While it is widely believed that the svm will perform substantially better than rlsc, we note that the same generalization bounds that apply to svms apply to rlsc, and we demonstrate empirically on both toy and real-world examples that rlscs performance is essentially equivalent to svms across a wide range of problems, implying that the choice between svm and rlsc should be based on computational tractability considerations Sale Ryan Rifkin Thesis

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