Dynamic label propagation for semi-supervised multi-class multi-label classification
نویسندگان
چکیده
منابع مشابه
Semi-supervised Learning for Multi-label Classification
In this report we consider the semi-supervised learning problem for multi-label image classification, aiming at effectively taking advantage of both labeled and unlabeled training data in the training process. In particular, we implement and analyze various semi-supervised learning approaches including a support vector machine (SVM) method facilitated by principal component analysis (PCA), and ...
متن کاملExploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کاملMulti Label Text Classification through Label Propagation
Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...
متن کاملSemi-supervised Latent Dirichlet Allocation for Multi-label Text Classification
This paper proposes a semi-supervised latent Dirichlet allocation (ssLDA) method, which differs from the existing supervised topic models for multi-label classification in mainly two aspects. Firstly both labeled and unlabeled learning data are used in ssLDA to train a model, which is very important for reducing the cost by manually labeling, especially when obtaining a fully labeled dataset is...
متن کاملSemi-Supervised Dimension Reduction for Multi-Label Classification
A significant challenge to make learning techniques more suitable for general purpose use in AI is to move beyond i) complete supervision, ii) low dimensional data and iii) a single label per instance. Solving this challenge would allow making predictions for high dimensional large dataset with multiple (but possibly incomplete) labelings. While other work has addressed each of these problems s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2016
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2015.10.006