• Publications
  • Influence
Enhancing Supervised Learning with Unlabeled Data
In a wide variety of supervised learning scenarios, there is a small set of labeled data, along with a large pool of unlabeled data. In this thesis, we present a new semi-supervised learning methodExpand
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Democratic co-learning
  • Y. Zhou, S. A. Goldman
  • Computer Science
  • 16th IEEE International Conference on Tools with…
  • 15 November 2004
For many machine learning applications it is important to develop algorithms that use both labeled and unlabeled data. We present democratic colearning in which multiple algorithms instead ofExpand
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An empirical assessment of long-branch attraction artefacts in deep eukaryotic phylogenomics.
In the context of exponential growing molecular databases, it becomes increasingly easy to assemble large multigene data sets for phylogenomic studies. The expected increase of resolution due to theExpand
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Heterotachy and long-branch attraction in phylogenetics
BackgroundProbabilistic methods have progressively supplanted the Maximum Parsimony (MP) method for inferring phylogenetic trees. One of the major reasons for this shift was that MP is much moreExpand
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A High-Performance Photovoltaic Module-Integrated Converter (MIC) Based on Cascaded Quasi-Z-Source Inverters (qZSI) Using eGaN FETs
  • Y. Zhou, Liming Liu, Hui Li
  • Engineering
  • IEEE Transactions on Power Electronics
  • 1 June 2013
This paper presents a single-phase grid-connected photovoltaic (PV) module-integrated converter (MIC) based on cascaded quasi-Z-source inverters (qZSI). In this system, each qZSI module serves as anExpand
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Investigation of a coupling model of coordination between urbanization and the environment.
China's coastal cities are experiencing rapid urbanization, which has resulted in many challenges. This paper presents a comprehensive index system for assessment of the level of urbanization basedExpand
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Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data
BackgroundResearchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently availableExpand
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Toward Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach
Model comparison for the purposes of selection, averaging, and validation is a problem found throughout statistics. Within the Bayesian paradigm, these problems all require the calculation of theExpand
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Minimum Spanning Tree Based Clustering Algorithms
The minimum spanning tree clustering algorithm is known to be capable of detecting clusters with irregular boundaries. In this paper, we propose two minimum spanning tree based clustering algorithms.Expand
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Adversarial support vector machine learning
Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it isExpand
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