Results 1 - 10 of 113

Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

by Bo Pang, Lillian Lee - In Proc. 43st ACL , 2005
"... We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an int ..."
Abstract - Cited by 298 (2 self) - Add to MetaCart
an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars ” is intuitively closer to “four stars ” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm

Active Learning for Large Multi-class Problems

by Prateek Jain, Ashish Kapoor
"... Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods for multi-class problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we ..."
Abstract - Cited by 53 (1 self) - Add to MetaCart
, we introduce a probabilistic variant of the K-Nearest Neighbor method for classification that can be seamlessly used for active learning in multi-class scenarios. Given some labeled training data, our method learns an accurate metric/kernel function over the input space that can be used

Eliciting Metrics for Accountability of Cloud Systems

by David Nuñez , Carmen Fernández-Gago , Jesús Luna
"... Abstract Cloud computing provides enormous business opportunities, but at the same time is a complex and challenging paradigm. The major concerns for users adopting the cloud are the loss of control over their data and the lack of transparency. Providing accountability to cloud systems could foster ..."
Abstract - Add to MetaCart
provide a methodology to elicit metrics for accountability in the cloud, which consists of three different stages. Since the nature of accountability attributes is very abstract and complex, in a first stage we perform a conceptual analysis of the accountability attributes in order to decompose them

Continuous Multiclass Labeling Approaches and Algorithms

by J. Lellmann - SIAM J. Imag. Sci , 2011
"... We study convex relaxations of the image labeling problem on a con-tinuous domain with regularizers based on metric interaction potentials. The generic framework ensures existence of minimizers and covers a wide range of relaxations of the originally combinatorial problem. We focus on two specific r ..."
Abstract - Cited by 28 (5 self) - Add to MetaCart
We study convex relaxations of the image labeling problem on a con-tinuous domain with regularizers based on metric interaction potentials. The generic framework ensures existence of minimizers and covers a wide range of relaxations of the originally combinatorial problem. We focus on two specific

MULTICLASS SUPPORT VECTOR MACHINES AND METRIC MULTIDIMENSIONAL SCALING FOR FACIAL EXPRESSION RECOGNITION

by Irene Kotsiay, Stefanos Zafeiriouy, Nikolaos Nikolaidisy, Ioannis Pitasy
"... In this paper, a novel method for the recognition of facial expressions in videos is proposed. The system rst extracts the deformed Candide facial grid that corresponds to the fa-cial expression depicted in the video sequence. The mean Euclidean distance of the deformed grids is then calculated to c ..."
Abstract - Add to MetaCart
to create a new metric multidimensional scaling. The clas-sication of the sample under examination to one of the 7 possible classes of facial expressions, i.e. anger, disgust, fear, happiness, sadness, surprise and neutral, is performed using multiclass SVMs dened in the new space. The ex-periments were

Multiclass object localization by combining local contextual interactions

by Carolina Galleguillos, Brian Mcfee, Serge Belongie, Gert Lanckriet - In CVPR , 2010
"... Recent work in object localization has shown that the use of contextual cues can greatly improve accuracy over models that use appearance features alone. Although many of these models have successfully explored different types of contextual sources, they only consider one type of contextual interact ..."
Abstract - Cited by 29 (4 self) - Add to MetaCart
interaction (e.g., pixel, region or object level interactions), leaving open questions about the true potential contribution of context. Furthermore, contributions across object classes and over appearance features still remain unknown. In this work, we introduce a novel model for multiclass object

CoMoM: Efficient Class-Oriented Evaluation of Multiclass Performance Models

by Giuliano Casale , 2009
"... We introduce the Class-oriented Method of Moments (CoMoM), a new exact algorithm to compute performance indexes in closed multiclass queueing networks. Closed models are important for performance evaluation of multi-tier applications, but when the number of service classes is large they become too ..."
Abstract - Cited by 4 (3 self) - Add to MetaCart
We introduce the Class-oriented Method of Moments (CoMoM), a new exact algorithm to compute performance indexes in closed multiclass queueing networks. Closed models are important for performance evaluation of multi-tier applications, but when the number of service classes is large they become too

MULTICLASS TEST FEATURE CLASSIFIER FOR TEXTURE CLASSIFICATION

by Satoru Igarashi, Vakthang Lashkia
"... A new multi-class pattern classifier called ‘Test Feature Classifier ’ is presented. It is based on training a recognis er by training samples of binary patterns and voting primitive scores depending on many trained templates called ‘test feature’, which serves as local evaluation of the features. T ..."
Abstract - Add to MetaCart
. The method is non-metric and does not misclassify any patterns once learned previously. The two-class version of test feature classifier was of high performance for searching textual region in complex images. In this paper, we extend it to handle multi-class problems and apply it for solving ill

Multi-Class Leveraged k-NN for Image Classification

by Paolo Piro, Richard Nock, Frank Nielsen, Michel Barlaud , 2012
"... Abstract. The k-nearest neighbors (k-NN) classification rule is still an essential tool for computer vision applications, such as scene recognition. However, k-NN still features some major drawbacks, which mainly reside in the uniform voting among the nearest prototypes in the feature space. In this ..."
Abstract - Cited by 5 (4 self) - Add to MetaCart
. In this paper, we propose a new method that is able to learn the “relevance ” of prototypes, thus classifying test data using a weighted k-NN rule. In particular, our algorithm, called Multi-class Leveraged k-nearest neighbor (MLNN), learns the prototype weights in a boosting framework, by minimizing a

Performance metrics and evaluation issues for continuous activity recognition,” in Performance Metrics for Intelligent Systems

by David Minnen, Tracy Westeyn, Thad Starner , 2006
"... Abstract — In this paper we examine several factors that influence the evaluation of multi-class, continuous activity recognition. Currently, there is no standard metric for evaluating and comparing such systems, although many possible error formulations and performance metrics could be adapted from ..."
Abstract - Cited by 13 (2 self) - Add to MetaCart
Abstract — In this paper we examine several factors that influence the evaluation of multi-class, continuous activity recognition. Currently, there is no standard metric for evaluating and comparing such systems, although many possible error formulations and performance metrics could be adapted
Results 1 - 10 of 113