JMLR
2012
12 years 1 months ago
2012
We present the first PAC bounds for learning parameters of Conditional Random Fields [12] with general structures over discrete and real-valued variables. Our bounds apply to com...
AI
2011
Springer
13 years 3 months ago
2011
Springer
Abstract. In this work, we present a method for classifying the quality of blog comments using Linear-Chain Conditional Random Fields (CRFs). This approach is found to yield high a...
SIAMIS
2010
13 years 6 months ago
2010
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
TIST
2011
13 years 6 months ago
2011
Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogue...
ICRA
2009
IEEE
13 years 9 months ago
2009
IEEE
Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performanc...
SAC
2008
ACM
13 years 11 months ago
2008
ACM
We present an analogy between the operation of a Wireless Sensor Network and the sampling and reconstruction of a signal. We measure the impact of three factors on the quality of ...
TIT
2008
13 years 11 months ago
2008
We consider the problem of sequential decision making for random fields corrupted by noise. In this scenario, the decision maker observes a noisy version of the data, yet judged wi...
NIPS
2008
14 years 28 days ago
2008
We introduce a new interpretation of multiscale random fields (MSRFs) that admits efficient optimization in the framework of regular (single level) random fields (RFs). It is base...
WSC
2008
14 years 1 months ago
2008
We are interested in computing tail probabilities for the maxima of Gaussian random fields. In this paper, we discuss two special cases: random fields defined over a finite number...
AAAI
2008
14 years 1 months ago
2008
Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed...