#### Filter Results:

- Full text PDF available (83)

#### Publication Year

2005

2017

- This year (15)
- Last 5 years (110)
- Last 10 years (129)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

#### Method

#### Organism

Learn More

- Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya
- SAC
- 2006

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What we need is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid… (More)

- Jaideep Vaidya, Hwanjo Yu, Xiaoqian Jiang
- Knowledge and Information Systems
- 2007

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid… (More)

- Hwanjo Yu, Jaideep Vaidya, Xiaoqian Jiang
- PAKDD
- 2006

Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharing of data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do… (More)

- Xiaoqian Jiang, Melanie Osl, Jihoon Kim, Lucila Ohno-Machado
- JAMIA
- 2012

OBJECTIVE
Predictive models that generate individualized estimates for medically relevant outcomes are playing increasing roles in clinical care and translational research. However, current methods for calibrating these estimates lose valuable information. Our goal is to develop a new calibration method to conserve as much information as possible, and would… (More)

- Yuan Wu, Xiaoqian Jiang, Jihoon Kim, Lucila Ohno-Machado
- JAMIA
- 2012

OBJECTIVE
The classification of complex or rare patterns in clinical and genomic data requires the availability of a large, labeled patient set. While methods that operate on large, centralized data sources have been extensively used, little attention has been paid to understanding whether models such as binary logistic regression (LR) can be developed in a… (More)

- Xiaoqian Jiang, Wanhong Xu, Latanya Sweeney, Yiheng Li, Ralph Gross, Daniel Yurovsky
- 2007 IEEE International Conference on Image…
- 2007

The ability to quickly compute hand geometry measurements from a freely posed hand offers advantages to biometric identification systems. While hand geometry systems are not new, typical measurements of lengths and widths of fingers and palms require rigid placement of the hand against pegs. Slight deviations in hand position, finger stretch or pressure can… (More)

- Xiaoqian Jiang, Bing Dong, Le Xie, Latanya Sweeney
- ECAI
- 2010

We study the problem of short term wind speed prediction, which is a critical factor for effective wind power generation. This is a challenging task due to the complex and stochastic behavior of the wind environment. Observing various periods in the wind speed time series present different patterns, we suggest a nonlinear adaptive framework to model various… (More)

- Lucila Ohno-Machado, Vineet Bafna, +12 authors Staal A. Vinterbo
- JAMIA
- 2012

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool… (More)

In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric technique offers both good ranking and… (More)

- Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang, Lide Wu
- ICML
- 2009

In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse Higher Order Conditional Random Fields (SHO-CRFs), which are able to handle local features and sparse higher order features together using a novel tractable exact inference… (More)