Browse by Subject "machine learning"
Now showing items 31-39 of 39
(iSchools, 2014-03-01)Qualitative content analysis is commonly used by social scientists to understand the practices of the groups they study, but it is often infeasible to manually code a large text corpus within a reasonable time frame and ...
(2015-01-21)Machine learning (ML) based inference has recently gained importance as a key kernel in processing massive data in digital signal processing (DSP) systems. Due to the ever increasing complexity of DSP systems, energy-efficient ...
(2005-05)The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. A particular setting of ...
(2013-02-03)Lack of human prior knowledge is one of the main reasons that the semantic gap still remains when it comes to automatic multimedia understanding. One difference between the human cognition system and state-of-the-art machine ...
Towards Accurate and Efficient Classification: A Discriminative and Frequent Pattern-Based Approach (2008-05)Classification is a core method widely studied in machine learning, statistics, and data mining. A lot of classification methods have been proposed in literature, such as Support Vector Machines, Decision Trees, and Bayesian ...
(2014-05-30)We begin by giving a comprehensive literature review that ties together many fields which have heretofore remained separate. We comment on the approaches from each field and show which algorithms are similar and which are ...
(2008-07)The aim of transfer learning is to reduce sample complexity required to solve a learning task by using information gained from solving related tasks. Transfer learning has in general been motivated by the observation that ...
(2012-05-22)Current analyses of groundwater flow and transport typically rely on a physically-based model (PBM), which is inherently subject to error and uncertainty from multiple sources including model structural error, parameter ...
Using machine learning models to interpret disciplinary styles of metadiscourse in dissertation abstracts (iSchools, 2013-02)This paper presents the results of a study of disciplinary stylistic differences among dissertation abstracts from physics, psychology, and philosophy. Based on differences in relative frequencies of metadiscourse terms ...