Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 8 of 8 for “"label prediction"”.
-
Development and Application of Network Algorithms for Prediction of Gene Function and Response to Viral Infection and Chemicals
… highly-scalable algorithm for network-based label prediction methods that enables the integration of functional annotations and interaction networks across many species in order to predict the functions of genes in newly-sequenced bacteria. 2. To overcome the limitations of experimental …
-
Structured support vector machines learning and application in computer vision
Image labeling tasks have been a long standing challenge in computer vision. In recent years, Markov /Conditional Random Fields (MRFs/CRFs) have gained popularity for the concept of "structured" learning, by defining proper pairwise potential functions to represent the spatial correlations among …
-
Structured support vector machines learning and application in computer vision
Image labeling tasks have been a long standing challenge in computer vision. In recent years, Markov /Conditional Random Fields (MRFs/CRFs) have gained popularity for the concept of "structured" learning, by defining proper pairwise potential functions to represent the spatial correlations among …
-
Robust graph transduction
… graph, graph transduction aims to assign unlabeled examples explicit class labels rather than build a general decision function based on the available labeled examples. Practically, a dataset usually contains many noisy data, such as the “bridge points” located across different classes, and …
-
Semi-Supervised Learning for Scalable and Robust Visual Search
… learning paradigm, in which a small set of labeled data is complemented with large unlabeled datasets. Graph-based approaches have emerged as methods of choice for general semi-supervised tasks when no parametric information is available about the data distribution. It treats both labeled …
-
Predicting object occupancy on the floor from RGBD images of indoor scenes
… recall rates. Using this algorithm improves the label predictions.
-
SCALABLE GRAPH REPRESENTATIONAL LEARNING ALGORITHMS FOR NETWORK MEDICINE
… from gene-disease prioritization to drug-target prediction or drug repurposing can be modelled as node label or edge prediction problems in graphs, where nodes represent bio-medical entities such as genes, drugs or diseases and edges interactions or relationships between them. Recently Graph …
-
Learning from multiple heterogeneous sources - Handling source trustworthiness and incompleteness
… multiple sources can improve the quality of the labels and extend the size of the training data. However, these information sources have their own properties, and cannot be directly combined and utilized. We study the source heterogeneity from two major aspects, i.e. (1) heterogeneous quality (2) …