Semi-supervised learning literature survey
XJ Zhu - 2005 - minds.wisconsin.edu
We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …
A brief survey on sequence classification
Sequence classification has a broad range of applications such as genomic analysis,
information retrieval, health informatics, finance, and abnormal detection. Different from the …
information retrieval, health informatics, finance, and abnormal detection. Different from the …
Evaluating protein transfer learning with TAPE
Protein modeling is an increasingly popular area of machine learning research. Semi-
supervised learning has emerged as an important paradigm in protein modeling due to the …
supervised learning has emerged as an important paradigm in protein modeling due to the …
Rolx: structural role extraction & mining in large graphs
Given a network, intuitively two nodes belong to the same role if they have similar structural
behavior. Roles should be automatically determined from the data, and could be, for …
behavior. Roles should be automatically determined from the data, and could be, for …
Machine learning and its applications to biology
The term machine learning refers to a set of topics dealing with the creation and evaluation
of algorithms that facilitate pattern recognition, classification, and prediction, based on …
of algorithms that facilitate pattern recognition, classification, and prediction, based on …
A survey of kernel and spectral methods for clustering
Clustering algorithms are a useful tool to explore data structures and have been employed
in many disciplines. The focus of this paper is the partitioning clustering problem with a …
in many disciplines. The focus of this paper is the partitioning clustering problem with a …
[كتاب][B] Semi-supervised learning with graphs
X Zhu - 2005 - search.proquest.com
In traditional machine learning approaches to classification, one uses only a labeled set to
train the classifier. Labeled instances however are often difficult, expensive, or time …
train the classifier. Labeled instances however are often difficult, expensive, or time …
[HTML][HTML] A cluster-then-label semi-supervised learning approach for pathology image classification
Completely labeled pathology datasets are often challenging and time-consuming to obtain.
Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points …
Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points …
Hydra: Large-scale social identity linkage via heterogeneous behavior modeling
We study the problem of large-scale social identity linkage across different social media
platforms, which is of critical importance to business intelligence by gaining from social data …
platforms, which is of critical importance to business intelligence by gaining from social data …
Multiple sequence alignment
Multiple sequence alignments are an essential tool for protein structure and function
prediction, phylogeny inference and other common tasks in sequence analysis. Recently …
prediction, phylogeny inference and other common tasks in sequence analysis. Recently …