Probabilistic Interpretations and Extensions for a Family of 2D PCA-style Algorithms (2009)
Shipeng Yu, Jinbo Bi, Jieping Ye
Recently there have been several 2D or higher-order PCAstyle dimensionality reduction algorithms, but they mostly lack probabilistic interpretations and are difficult to apply with, e.g., incomplete...
Large Scale Diagnostic Code Classification for Medical Patient Records (2009)
Lucian Vlad Lita, Shipeng Yu, Stefan Niculescu, Jinbo Bi
A critical, yet not very well studied problem in medical applications is the issue of accurately labeling patient records according to diagnoses and procedures that patients have undergone. This...
Siemens Medical Solutions (2009)
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single positive...
Abstract Probabilistic Joint Feature Selection for Multi-task Learning (2009)
Tao Xiong, Jinbo Bi, Bharat Rao, Vladimir Cherkassky
We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypothesis classes...
Computer-Aided Diagnosis Group, Siemens Medical Solutions, Malvern, PA (2008)
Jianwu Xu, Advisor Dr, Jose C. Principe, Advisors Drs, Shipeng Yu, Jinbo Bi, ...
Bayesian data analysis, learning with unlabeled data
ABSTRACT On the Medical Frontier: The 2006 KDD Cup Competition and Results (2008)
Terran Lane, Bharat Rao, Jinbo Bi, Jianming Liang, Marcos Salganicoff
tasks drawn from a medical imaging domain. At the core, all of these tasks were concerned with identifying pulmonary embolisms (PEs) from pre-processed computed tomography (CT) images of human lungs....
Automatic Medical Coding of Patient Records via Weighted Ridge Regression (2008)
Jianwu Xu, Shipeng Yu, Jinbo Bi, Lucian Vlad Lita, Stefan Niculescu, R. Bharat Rao
In this paper, we apply weighted ridge regression to tackle the highly unbalanced data issue in automatic largescale ICD-9 coding of medical patient records. Since most of the ICD-9 codes are...
Sparse classifiers for Automated Heart Wall Motion Abnormality Detection (2008)
Glenn Fung, Maleeha Qazi, Sriram Krishnan, Jinbo Bi, Bharat Rao
Coronary Heart Disease is the single leading cause of death world-wide, with lack of early diagnosis being a key contributory factor. This disease can be diagnosed by measuring and scoring regional...
Knowledge Solutions Group (2008)
We propose a novel classification approach for automatically detecting pulmonary embolism (PE) from computedtomography-angiography images. Unlike most existing approaches that require vessel...
Large Scale Diagnostic Code Classification for Medical Patient Records (2008)
Lucian Vlad Lita, Shipeng Yu, Stefan Niculescu, Jinbo Bi
A critical, yet not very well studied problem in medical applications is the issue of accurately labeling patient records according to diagnoses and procedures that patients have undergone. This...
ABSTRACT On the Medical Frontier: The 2006 KDD Cup Competition and Results (2008)
Terran Lane, Bharat Rao, Jinbo Bi, Jianming Liang, Marcos Salganicoff
tasks drawn from a medical imaging domain. At the core, all of these tasks were concerned with identifying pulmonary embolisms (PEs) from pre-processed computed tomography (CT) images of human lungs....
Abstract Probabilistic Joint Feature Selection for Multi-task Learning (2008)
Tao Xiong, Jinbo Bi, Bharat Rao, Vladimir Cherkassky
We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypothesis classes...
Automated Heart Wall Motion Abnormality Detection using Sparse Linear Classifiers (2008)
Maleeha Qazi, Glenn Fung, Sriram Krishnan, Jinbo Bi, R. Bharat Rao, Alan Katz
Coronary Heart Disease is the single leading cause of death world-wide, with lack of early diagnosis being a key contributory factor. This disease can be diagnosed by measuring and scoring regional...
Siemens Medical Solutions (2008)
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single positive...
Abstract A Geometric Approach to Support Vector Regression (2008)
We develop an intuitive geometric framework for support vector regression (SVR). By examining when ɛ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard...
Automatic View Recognition for Cardiac Ultrasound Images (2008)
Matthew Eric Otey, Jinbo Bi, Sriram Krishnan, Bharat Rao, Jonathan Stoeckel, Alan Katz, ...
Abstract. Ultrasound images of the heart can be taken from many different angles. Diagnostic analysis of these images requires recognizing the pose of the heart so that important cardiac structures...
LungCAD: A Clinically Approved, Machine Learning System for Lung Cancer Detection ABSTRACT (2008)
R Bharat Rao, Jinbo Bi, Glenn Fung Marcos
We present LungCAD, a computer aided diagnosis (CAD) system that employs a classification algorithm for detecting solid pulmonary nodules from CT thorax studies. We briefly describe some of the...
A Mathematical Programming Formulation for Sparse Collaborative Computer Aided Diagnosis (2008)
A mathematical programming formulation is proposed to eliminate irrelevant and redundant features for collaborative computer aided diagnosis which requires to detect multiple clinically-related...
Sparse classifiers for Automated Heart Wall Motion Abnormality Detection (2008)
Glenn Fung, Maleeha Qazi, Sriram Krishnan, Jinbo Bi, Bharat Rao
Coronary Heart Disease is the single leading cause of death world-wide, with lack of early diagnosis being a key contributory factor. This disease can be diagnosed by measuring and scoring regional...
Learning classifiers when the training data is not IID (2007)
Murat Dundar, Balaji Krishnapuram, Jinbo Bi, R. Bharat Rao
Most methods for classifier design assume that the training samples are drawn independently and identically from an unknown data generating distribution, although this assumption is violated in...
Computer aided detection of pulmonary embolism with Tobogganing in CT angiography (2007)
Abstract. Pulmonary embolism (PE) is a very serious condition causing sudden death in about one-third of the cases. Treatment with anti-clotting medications is highly effective but not without...
Joint optimization of cascaded classifiers for computer aided detection (2007)
The existing methods for offline training of cascade classifiers take a greedy search to optimize individual classifiers in the cascade, leading inefficient overall performance. We propose a new...
Learning classifiers when the training data is not IID (2007)
Murat Dundar, Balaji Krishnapuram, Jinbo Bi, R. Bharat Rao
Most methods for classifier design assume that the training samples are drawn independently and identically from an unknown data generating distribution, although this assumption is violated in...
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers (2006)
Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Toshiro Kubota, Glenn Fung, Marcos Salganicoff, ...
This paper describes a novel classification method for computer aided detection (CAD) that identifies structures of interest from medical images. CAD problems are challenging largely due to the...
Active learning via transductive experimental design (2006)
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple concept for active...
Active learning via transductive experimental design (2006)
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple concept for active...
Active learning via transductive experimental design (2006)
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple concept for active...
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers (2006)
Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Toshiro Kubota, Glenn Fung, Marcos Salganicoff, ...
This paper describes a novel classification method for computer aided detection (CAD) that identifies structures of interest from medical images. CAD problems are challenging largely due to the...
Efficient model selection for regularized linear discriminant analysis (2006)
Jieping Ye, Ravi Janardan, Vladimir Cherkassky, Tao Xiong, Jinbo Bi, Chandra Kambhamettu
Classical Linear Discriminant Analysis (LDA) is not applicable for small sample size problems due to the singularity of the scatter matrices involved. Regularized LDA (RLDA) provides a simple...
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers (2006)
Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Toshiro Kubota, Glenn Fung, Marcos Salganicoff, ...
This paper describes a novel classification method for computer aided detection (CAD) that identifies structures of interest from medical images. CAD problems are challenging largely due to the...
Transductive Experiment Design (2005)
Kai Yu, Jinbo Bi, Volker Tresp
This paper considers the problem of selecting the most informative experiments x to get measures y for learning an inference model y = f(x). We propose a novel concept for active learning,...
A sparse support vector machine approach to region-based image categorization (2005)
Jinbo Bi, Yixin Chen, James Z. Wang
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance...
Semi-supervised mixture of kernels via lpboost methods (2005)
Jinbo Bi, Glenn Fung, Murat Dundar, Bharat Rao
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based on one kernel choice...
Semi-supervised mixture of kernels via lpboost methods (2005)
Jinbo Bi, Glenn Fung, Murat Dundar, Bharat Rao
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. Unlike traditional kernel methods which select a kernel according to cross...
Semi-supervised mixture of kernels via lpboost methods (2005)
Jinbo Bi, Glenn Fung, Murat Dundar, Bharat Raocomputer, Aided Diagnosis, Therapy Solutions
Abstract We propose an algorithm to construct classification mod-els with a mixture of kernels from labeled and unlabeled data. Unlike traditional kernel methods which select a ker-nel according to...
A fast iterative algorithm for fisher discriminant using heterogeneous kernels (2004)
Glenn Fung, Murat Dundar, Jinbo Bi, Bharat Rao
We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a...
A Fast Iterative Algorithm for Fisher Discriminant using (2004)
Heterogeneous Kernels Glenn, Glenn Fung, Murat Dundar, Jinbo Bi, Bharat Rao
We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a...
A fast iterative algorithm for fisher discriminant using heterogeneous kernels (2004)
Glenn Fung, Murat Dundar, Jinbo Bi, Bharat Rao
We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a...
Support vector classification with input data uncertainty (2004)
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the statistical reasoning, we...
A fast iterative algorithm for fisher discriminant using heterogeneous kernels (2004)
Glenn Fung, Murat Dundar, Jinbo Bi, Bharat Rao
We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a...
Support vector classification with input data uncertainty (2004)
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the statistical reasoning, we...
Dimensionality Reduction via Sparse Support Vector Machines (2003)
Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...
We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...
Dimensionality Reduction via Sparse Support Vector Machines (2003)
Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...
We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...
Dimensionality Reduction via Sparse Support Vector Machines (2003)
Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...
We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...
Dimensionality Reduction via Sparse Support Vector Machines (2003)
Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...
We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...
Multi-objective programming in SVMs (2003)
We propose a general framework for support vector machines (SVM) based on the principle of multi-objective optimization. The learning of SVMs is formulated as a multiobjective program (MOP) by...
Learning with rigorous support vector machines (2003)
Abstract. We examine the so-called rigorous support vector machine (RSVM) approach proposed by Vapnik (1998). The formulation of RSVM is derived by explicitly implementing the structural risk...
Regression Error Characteristic CurVes (2003)
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to...
Duality, Geometry, and Support Vector Regression (2002)
We develop an intuitive geometric framework for support vector regression (SVR). By examining when ɛ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard...
Duality, Geometry, and Support Vector Regression (2002)
We develop an intuitive geometric framework for support vector regression (SVR). By examining when #-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard...
Duality, Geometry, and Support Vector Regression (2002)
We develop an intuitive geometric framework for support vector regression (SVR). By examining when #-tubes exist, we show that SVR can be regarded a classification problem in the dual space. Hard and...
Support Vector Regression With Applications In Automated Drug Discovery (2002)
v 1. Variable Selection via Sparse Support Vector Machines . . . . . . . . . . . 1 1.1
Duality, Geometry, and Support Vector Regression (2002)
We develop an intuitive geometric framework for support vector regression (SVR). By examining when ǫ-tubes exist, we show that SVR can be regarded a classification problem in the dual space. Hard...