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Tomaso Poggio

Known as: Poggio, Tomasio Poggio 
Tomaso Armando Poggio (born September 11, 1947 in Genoa, Italy), is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences… 
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Related topics

Related topics

6 relations
CBCL (MIT)Computational neuroscienceHierarchical temporal memoryICPRAM

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Programming language processing (similar to natural language processing) is a hot research topic in the field of software… 
Highly Cited
2007
Highly Cited
2007
Optimal Rates for the Regularized Least-Squares Algorithm
We develop a theoretical analysis of the performance of the regularized least-square algorithm on a reproducing kernel Hilbert… 
Highly Cited
2006
Highly Cited
2006
Multiclass Object Recognition with Sparse, Localized Features
We apply a biologically inspired model of visual object recognition to the multiclass object categorization problem. Our model… 
Highly Cited
2005
Highly Cited
2005
Core Vector Machines: Fast SVM Training on Very Large Data Sets
Standard SVM training has O(m3) time and O(m2) space complexities, where m is the training set size. It is thus computationally… 
Highly Cited
2005
Highly Cited
2005
Multicategory Proximal Support Vector Machine Classifiers
Given a dataset, each element of which labeled by one of k labels, we construct by a very fast algorithm, a k-category proximal… 
Highly Cited
2002
Highly Cited
2002
Efficient SVM Regression Training with SMO
The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines… 
Highly Cited
2001
Highly Cited
2001
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other… 
Highly Cited
1998
Highly Cited
1998
Probabilistic modeling of local appearance and spatial relationships for object recognition
In this paper, we describe an algorithm for object recognition that explicitly models and estimated the posterior probability… 
Highly Cited
1990
Highly Cited
1990
Extensions of a Theory of Networks for Approximation and Learning
Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional… 
Highly Cited
1990
Highly Cited
1990
Networks and the best approximation property
Networks can be considered as approximation schemes. Multilayer networks of the perceptron type can approximate arbitrarily well… 
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