Very Short Literature Survey From Supervised Learning To Surrogate Modeling

A Literature Survey on Domain Adaptation of Statistical Classifiers

The domain adaptation problem, especially domain adaptation in natural language processing, started gaining much attention very recently [Daumé III and Marcu, 2006, Blitzer et al., 2006, Ben-David et al., 2007, Daumé III, 2007, Satpal and Sarawagi, 2007]. However, some special kinds of domain adaptation problems have been studied before under different names such as class imbalance [Japkowicz a...

Semi-Supervised Learning Literature Survey

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

During the past few years, the machine learning community has paid attention to developing new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce t...

β-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

During the past few years, the machine learning community has paid attention to developing new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce t...

Emotion Detection in Persian Text; A Machine Learning Model

This study aimed to develop a computational model for recognition of emotion in Persian text as a supervised machine learning problem. We considered Pluthchik emotion model as supervised learning criteria and Support Vector Machine (SVM) as baseline classifier. We also used NRC lexicon and contextual features as training data and components of the model. One hundred selected texts including pol...