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Giorgos Zacharia Theodoros Evgeniou Alexandros Moukas Petros Boufounos Pattie Maes 《Electronic Commerce Research》2001,1(1-2):85-100
We present a framework to study the microeconomic effects in a reputation brokered Agent mediated Knowledge Marketplace, when we introduce dynamic pricing algorithms. We study the market with computer simulations of multiagent interactions. In this marketplace, the seller reputations are updated in a collaborative fashion based on the performance of the user in the delegated tasks. To the best of our knowledge this is the first agent mediated marketplace where the agents use dynamic pricing based on dynamically updated reputations. The framework can be used to investigate the different equilibria reached, based on the level of intelligence of the selling agents, the level of price-importance elasticity of the buying agents, and the level of unemployment in the marketplace. Preliminary experiments addressing these issues are presented. 相似文献
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Statistical Learning Theory: A Primer 总被引:2,自引:0,他引:2
Evgeniou Theodoros Pontil Massimiliano Poggio Tomaso 《International Journal of Computer Vision》2000,38(1):9-13
In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well as emerging learning techniques such as Regularization Networks and Support Vector Machines which can be justified in term of the same induction principle. 相似文献
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Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers 总被引:4,自引:0,他引:4
We study the leave-one-out and generalization errors of voting combinations of learning machines. A special case considered is a variant of bagging. We analyze in detail combinations of kernel machines, such as support vector machines, and present theoretical estimates of their leave-one-out error. We also derive novel bounds on the stability of combinations of any classifiers. These bounds can be used to formally show that, for example, bagging increases the stability of unstable learning machines. We report experiments supporting the theoretical findings. 相似文献
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Convex multi-task feature learning 总被引:2,自引:1,他引:1
We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the
well-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned
features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which
there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately
performs a supervised and an unsupervised step, where in the former step it learns task-specific functions and in the latter
step it learns common-across-tasks sparse representations for these functions. We also provide an extension of the algorithm
which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate
that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned
features common across related tasks. Our algorithm can also be used, as a special case, to simply select—not learn—a few
common variables across the tasks.
Editors: Daniel Silver, Kristin Bennett, Richard Caruana.
This is a longer version of the conference paper (Argyriou et al. in Advances in neural information processing systems, vol. 19,
2007a). It includes new theoretical and experimental results. 相似文献
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Abernethy J. Evgeniou T. Toubia O. Vert J.-P. 《Knowledge and Data Engineering, IEEE Transactions on》2007,19(5):614-630
We propose a framework for designing adaptive choice-based conjoint questionnaires that are robust to response error. It is developed based on a combination of experimental design and statistical learning theory principles. We implement and test a specific case of this framework using Regularization Networks. We also formalize within this framework the polyhedral methods recently proposed in marketing. We use simulations, as well as an online market research experiment with 500 participants, to compare the proposed method to benchmark methods. Both experiments show that the proposed adaptive questionnaires outperform the existing ones in most cases. This work also indicates the potential of using machine-learning methods in marketing. 相似文献
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Evgeniou T. Pontil M. Papageorgiou C. Poggio T. 《Knowledge and Data Engineering, IEEE Transactions on》2003,15(4):911-920
The success of a multimedia information system depends heavily on the way the data is represented. Although there are "natural" ways to represent numerical data, it is not clear what is a good way to represent multimedia data, such as images, video, or sound. We investigate various image representations where the quality of the representation is judged based on how well a system for searching through an image database can perform-although the same techniques and representations can be used for other types of object detection tasks or multimedia data analysis problems. The system is based on a machine learning method used to develop object detection models from example images that can subsequently be used for examples to detect-search-images of a particular object in an image database. As a base classifier for the detection task, we use support vector machines (SVM), a kernel based learning method. Within the framework of kernel classifiers, we investigate new image representations/kernels derived from probabilistic models of the class of images considered and present a new feature selection method which can be used to reduce the dimensionality of the image representation without significant losses in terms of the performance of the detection-search-system. 相似文献
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