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Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

by Gediminas Adomavicius, Alexander Tuzhilin - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
Abstract - Cited by 1490 (23 self) - Add to MetaCart
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes

An Outlook of State-of-the-Art Approaches in Functional Testing of Web Application

by M. Y. Suhaila, W. K. Wan, Mohd Nasir
"... Abstract — The numerous recent contribution of web application testing approaches reflect the rising awareness and concern for assuring web application’s functionality and performance. The functionality of a web application cannot be compromised, especially if dealing with critical web-based applica ..."
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. In this paper we firstly perform exhaustive literature reviews on stateof-the-art approaches in functional testing of web application. Next, a comparative evaluation framework that consists of several related criteria is developed, and subsequently applied to the selected prominent approaches. Finally, we

Object Detection with Discriminatively Trained Part Based Models

by Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
Abstract - Cited by 1422 (49 self) - Add to MetaCart
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular

Bayesian Network Classifiers

by Nir Friedman, Dan Geiger, Moises Goldszmidt , 1997
"... 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 C4.5. This fact raises the question of whether a classifier with less restr ..."
Abstract - Cited by 796 (20 self) - Add to MetaCart
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 C4.5. This fact raises the question of whether a classifier with less

Local features and kernels for classification of texture and object categories: a comprehensive study

by J. Zhang, S. Lazebnik, C. Schmid - International Journal of Computer Vision , 2007
"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."
Abstract - Cited by 653 (34 self) - Add to MetaCart
and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on four texture and five object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate

On the algorithmic implementation of multi-class kernel-based vector machines

by Koby Crammer, Yoram Singer, Nello Cristianini, John Shawe-taylor, Bob Williamson - Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
Abstract - Cited by 559 (13 self) - Add to MetaCart
significant running time improvements for large datasets. Finally, we describe various experiments with our approach comparing it to previously studied kernel-based methods. Our experiments indicate that for multiclass problems we attain state-of-the-art accuracy.

Sparse Bayesian Learning and the Relevance Vector Machine

by Michael E. Tipping , 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vect ..."
Abstract - Cited by 966 (5 self) - Add to MetaCart
vector machine’ (RVM), a model of identical functional form to the popular and state-of-the-art `support vector machine ’ (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis

Range-Free Localization Schemes for Large Scale Sensor Networks

by Tian He, Chengdu Huang, Brain M. Blum, John A. Stankovic, Tarek Abdelzaher , 2003
"... Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point-to-point distance estimates. Because coarse accura ..."
Abstract - Cited by 525 (8 self) - Add to MetaCart
performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition

Token flow control

by Amit Kumar, et al.
"... As companies move towards many-core chips, an efficient onchip communication fabric to connect these cores assumes critical importance. To address limitations to wire delay scalability and increasing bandwidth demands, state-of-the-art on-chip networks use a modular packet-switched design with route ..."
Abstract - Cited by 635 (35 self) - Add to MetaCart
As companies move towards many-core chips, an efficient onchip communication fabric to connect these cores assumes critical importance. To address limitations to wire delay scalability and increasing bandwidth demands, state-of-the-art on-chip networks use a modular packet-switched design

Linear spatial pyramid matching using sparse coding for image classification

by Jianchao Yang, Kai Yu, Yihong Gong, Thomas Huang - in IEEE Conference on Computer Vision and Pattern Recognition(CVPR , 2009
"... Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algo ..."
Abstract - Cited by 497 (21 self) - Add to MetaCart
on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors. 1.
Results 1 - 10 of 13,801