University of Florida

Date

Presenter

Info

15th Nov

Jeremy Bolton

Multiple Instance Hidden Markov Model and Applications to Landmine Detection in GPR Data

Date

Presenter

Info

25th Jan

Taylor Glenn

Non-Visual Scene Analysis for Improving Ground Penetrating Radar Based Detection Systems

Existing detection algorithms are being challenged by complex sensing environments and evolving and irregular target types. These challenges motivate a broader approach to improving performance by analyzing the entire sensor scene beyond simply the search for known targets. Specifically, environmental and sensor-specific factors not directly related to the targets (externalities) affect the ability of existing target detection algorithms to detect targets more accurately. This presentation will introduce some categories of externalities that limit performance, show results from alarm level non-visual scene analysis obtained using various algorithms, and motivate some long term research goals aimed at mitigating these problems caused by externalities.

18th Jan

Seniha Esen Yuksel

Person Recognition from Hyperspectral Iris Data

Abstract:
In this study, we gathered hyperspectral data from the eyes of 50 people at 1 meter, and from 20 of them at 3 meters. I will present our results in recognizing the people from their iris data, and show how similar the right and left eye of each person is. We will also see if a K-NN classifer trained with 1m. data can be successful in testing 3m data.

Date

Presenter

Info

4th Nov

Joshua Wood

Fuzzy Kernel C-means

presenting the paper: "Fuzzy C-means clustering algorithm based on kernel method" by Wu, Xie and Yu. link

21st Oct

Brandon Smock

Timbre and Musical Instrument Recognition

An overview of the nature of timbre, musical signals, Fourier analysis, the history of timbre research, and current developments in instrument recognition

14th Oct

Ryan Close

Got LiDAR?

An introduction to the principles of LiDAR, including: LiDAR system design, processing, segmentation, features, object classification, and current research trends

30th Sep

Dr. Jeff Ho

Title: Learning to Track Articulated Objects

Abstract:

In this talk, I will present two lines of research for tracking articulated objects in different scenarios. For images acquired at a distance, we propose an algorithm for accurate tracking of articulated objects using online update of appearance and shape. The challenge here is to model foreground appearance with histograms in a way that is both efficient and accurate. In this algorithm, the constantly changing foreground shape is modeled as a small number of rectangular blocks, whose positions within the tracking window are adaptively determined. Under the general assumption of stationary foreground appearance, we show that robust object tracking is possible by adaptively adjusting the locations of these blocks.

For images containing sufficient visual information, it is feasible to track articulated objects and estimate their 3D pose. A major challenge in applying Bayesian tracking methods for tracking 3D human body pose is the high dimensionality of the pose state space. The goal of this work is to approximate the low-dimensional manifold so that a low-dimensional state vector can be obtained for efficient and effective Bayesian tracking. To achieve this goal, a globally coordinated mixture of factor analyzers is learned from motion capture data. Each factor analyzer in the mixture is a locally linear dimensionality reducer that approximates a part of the manifold. The global parametrization of the manifold is obtained by aligning these locally linear pieces in a global coordinate system. Quantitative comparisons on benchmark datasets show that the proposed method produces more accurate 3D pose estimates over time than those obtained from two previously proposed Bayesian tracking methods.

23rd Sep

Clint George

Topic extraction and categorization using LDA

Abstract:

Talk will focus on the application of LDA to topic extraction and categorization of the web documents. This may include text preprocessing (stemming, lemmatization, etc), feature reduction using the LDA output, document classification, and experimental results using Wikipedia pages. In addition, the things that were not covered in the last presentation such as the LDA geometric interpretation, and other simple document models such as TF-IDF, LSI, PLSI, and Mixture of Unigrams

16th Sep

Taylor Glenn

Latent Dirichlet Allocation

Reference:

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, January 2003. link

Date

Presenter

Info

12th Aug

Christopher Ratto

Context-Dependent Learning for GPR-Based Target Detection

27th May

Claudio Fuentes

The Receiver Operating Characteristic Curve (A Brief Introduction)

Date

Presenter

Info

6th May

Ken Watford

Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data by P. Funk and N. Xiong

29th April

Sile Hu

Boosting Trees

References:

Hastie, Tibshirani and Friedman, "The Elements of Statistical Learning", 2nd edition. Chapter 10

Slides

22nd April

Ryan Close

Mixtures of Gaussian Processes

References:

C. E. Rasmussen and Z. Ghahramani. Infinite mixtures of Gaussian process experts. In T. G. Diettrich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14. The MIT Press, 2002.

V Tresp. Mixtures of Gaussian Processes. Advances in Neural Information Processing Systems 2001.

15th April

Seniha Esen Yuksel

Clustering HMMs

Related Papers:

1. Minimum classification error rate methods for speech recognition

2. Similarity-based classification of sequences using hidden Markov models, Pattern Recognition,2004

3. A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models, ICML 2000

4. Clustering Sequences with Hidden Markov Models, NIPS 1997

1st April

Dr. Alina Zare

Bachmann, C.M.; Ainsworth, T.L.; Fusina, R.A.; Montes, M.J.; Bowles, J.H.; Korwan, D.R.; Gillis, D.B.; ,
"Bathymetric Retrieval From Hyperspectral Imagery Using Manifold Coordinate Representations,"
Geoscience and Remote Sensing, IEEE Transactions on , vol.47, no.3, pp.884-897, March 2009

3rd March

Ganesan Ramachandran

Dissertation Defense Practice: Fast Physics Based Methods for Wideband Electromagnetic Induction

11th February

Ganesan Ramachandran

Joint Sparse Estimation of Dielectric Relaxations

related references:

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1453780&tag=1

http://stanford.edu/~boyd/papers/rwl1.html

http://linkinghub.elsevier.com/retrieve/pii/S0167739X03000438

http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05280238

4th February

Eunju Kim

Semantic Neural Networks for Scalable Activity Recognition

14th January

Taylor Glenn

Convolution Based Features in a Compressive Sensing Framework

related paper:

Ali Cafer Gurbuz, James H. McClellan, Justin Romberg, and Waymond R. Scott, Compressive sensing of parameterized shapes in images, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008

Date

Presenter

Paper

15th December Tuesday

Brandon Smock

TBD

11th December Friday

Oualid Missaoui, University of Louisville

Oualid Missaoui, Hichem Frigui, Paul Gader, Landmine Detection with Ground Penetrating Radar using Multi-Stream Discrete Hidden Markov Models

10th December Thursday

Dr. Jeremy Bolton

Multiple Instance Learning via Kernel Methods: MI-RVM and MI-SVM

Related Papers:
VC Raykar, B Krishnapuram, J Bi, M Dundar, Bayesian multiple instance learning: automatic feature selection and inductive transfer

S Andrews, I Tsochantaridis, T Hofmann, Support vector machines for multiple-instance learning

OE Kundakcioglu, O Seref, PM Pardalos, Multiple instance learning via margin maximization

8th December Tuesday

Joshua A. Horton

Model-based classification With Missing Data via Dynamic Programming

1st December Tuesday

Ryan Close

"Predictive Distributions: An Introduction to Gaussian Processes". Reference: Bishop section 6.4

24th November Tuesday

Ryan Close

"Predictive Distributions: An Introduction to Gaussian Processes". Reference: Bishop section 6.4

19th November Thursday, 2:00pm in Rm305

Xuping Zhang

Dissertation defense: Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling

17th November Tuesday

Dr. Alina Zare

Jia, S.; Qian, Y., Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol.47, no.1, pp.161-173, Jan. 2009

12th November Thursday, 11:00am in Rm 305

Xuping Zhang

Dissertation defense practice: Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling

11th November Wednesday, 10:30am in Rm440

Dr. Magdi Mohamed

M.A. Mohamed and Weimin Xiao, Q-metrics: An efficient formulation of normalized distance functions, IEEE Int. Conf. on Systems, Man and Cybernetics, 2007

10th November Tuesday

Gyeongyong Heo

Gyeongyong Heo PhD defense: Robust Kernel Methods in Context-Dependent Fusion

5th November Thursday

Ken Watford

Reinforcement Learning

3rd November Tuesday

Gyeongyong Heo

Defense practice talk: Robust Kernel Methods in Context-Dependent Fusion

27th October

Taylor Glenn

Ali Cafer Gurbuz, James H. McClellan, Justin Romberg, and Waymond R. Scott, Compressive sensing of parameterized shapes in images, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008

8th October

Prof. George Casella

Estimation in Dirichlet Random Effects Models, Annals of Statistics

6th October

Seniha Esen Yuksel

Variational Learning of Mixture of Experts for Classification Cont'd.

1) Classification and Regression using Mixtures of Experts, Steven Richard Waterhouse, 1997
2) Bayesian methods for mixtures of experts , Steve Waterhouse, David Mackay, Tony Robinson, Nips 1996
3) Hierarchical mixture of experts and the em algorithm, Michael I. Jordan and Robert A. Jacobs
4) convergence results for the em approach to mixture of experts architecture

29th September

Seniha Esen Yuksel

Variational Learning of Mixture of Experts for Classification

1) Classification and Regression using Mixtures of Experts, Steven Richard Waterhouse, 1997
2) Bayesian methods for mixtures of experts , Steve Waterhouse, David Mackay, Tony Robinson, Nips 1996
3) Hierarchical mixture of experts and the em algorithm, Michael I. Jordan and Robert A. Jacobs
4) convergence results for the em approach to mixture of experts architecture

Date

Presenter

Paper

23th July

Seniha Esen Yuksel

Distance measures for Hidden Markov Models

1) A Probabilistic Distance Measure for Hidden Markov Models, B. H. Juang and L. R. Rabiner, AT&T Technical Journal, Vol. 64, No. 2, pp. 391-408, February 1985
2) Calculation Of Distance Measures Between Hidden Markov Models , in Proc. Eurospeech, 1995
3) Two dissimilarity measures for HMMs and their application in phoneme model clustering, in ICASSP 02.

25th June

Ganesh Ramachandran

Fast physics-based methods for wideband EMI data analysis

18th June

Dr. Jeremy Bolton

Random Set Framework for Multiple Instance Learning: Application to GPR Data

Related Paper:
Maron, O. and Ratan, A.L. Multiple-Instance Learning for Natural Scene Classification , Proceedings of the Fifteenth International Conference on Machine Learning, 1998, pp.341-349

11th June

Seniha Esen Yuksel

Qi Yuting , J.W. Paisley, L. Carin, "Dirichlet Process HMM Mixture Models with Application to Music Analysis," ICASSP 2007

7-10th June

CSI:Florida

Collaborators meeting: Presentations @ Duke Univ.

4th June

Ryan Close

PRML Book by Bishop, Chapters 3.3 Bayesian Linear Regression, 3.5 The Evidence Approximation, 7.1 SVM, 7.2 Relevance Vector Machines

1st June

Xuping Zhang

Thesis Proposal: "Image based automatic feature learning and classification" in Rm 305, at 10:00am.

28th May

Karthik Gurumoorthy

K. Gurumoorthy and A. Rangarajan, A Schrodinger Equation for the Fast Computation of Approximate Euclidean Distance Functions , SSVM 2009.

21th May

Taylor Glenn

GPR Processing with Corrdet and LPP
1. Corrdet Paper
2. LPP Paper (section 4.1)

14th May

Nathan Vanderkraats

A Task-Based Approach to Decoding Auditory Spiketrain Information

7th May

Ganesh Ramachandran

Robust Estimation of the Discrete Spectrum of Relaxations For Electromagnetic Induction Responses

Date

Presenter

Paper

30th April

Dr. Alina Zare

"Isomap Algorithm for Nonlinear Dimensionality Reduction," based on the following paper:
1. Tenenbaum, et al. A Global Geometric Framework for Nonlinear Dimensionality Reduction

Additional Reading:

1. Balasubramanian, et al The Isomap Algorithm and Topological Stability
2. Bachmann, et al. Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes
3. Bachmann et al., "Exploiting Manifold Geometry in Hyperspectral Imagery"
4. DeSilva, et al., "Global versus local methods in nonlinear dimensionality reduction"

23th April

Brandon Smock

New features for landmine detection

9th April

Dr. Todd Schultz and Saket Kharsikar

MathWorks will be presenting "Speeding Up MATLAB Applications and using MATLAB in the Life Sciences"
9:30am to 11:30am: Speeding up MATLAB Applications
1:00pm to 2:30pm: Data Analysis using MATLAB in Life Sciences.

2nd April

Dr. Jeremy Bolton

"Human Terrain Systems," based on the following papers:
1. Human Terrain Systems
2. The Human Terrain System: A CORDS for the 21st Century
3. Human Terrain System
4. L. Bruzzone, D.F. Prieto, "An adaptive semiparametric and context-based approach tounsupervised change detection in multitemporal remote-sensing Images," IEEE Transactions on Image Processing, Apr 2002, vol. 11, issue 4, pp. 452-466

24th March

Dr. Simon Haykin, McMaster University

IEEE Gainesville Section Presents Enabling New Research Directions in Engineering with Cognition: The Cognitive Radio Example.
Dr. Simon Haykin, University Professor, Director of Cognitive Systems Laboratory, McMaster University, Hamilton, ON, Canada
Tuesday, March 24, 2009 11:45 AM - 12:35 PM 201 New Engineering Building (Center Drive, University of Florida)

19th March

Gyeongyong Heo

PhD Proposal: Robust Kernel Methods in Context-Dependent Fusion

12th March

Seniha Esen Yuksel

Padhraic Smyth; "Clustering sequences with hidden Markov models," Advances in Neural Information Processing Systems, 1997.

5th March

Raazia Mazhar

PhD Dissertation Defense: Optimized Dictionary Design and Classification Using the Matching Pursuits Dissimilarity Measure

24 - 27 February

AMDS and GSTAMIS AWG Meetings

Algorithms Working Group Presentations from UFL, Duke Univ, NITEK, BAE, and Cyterra.

19th February

Dr. Jeremy Bolton

"Multiple Instance Learning: Diverse Density," based on the following papers:
1. Maron, O. and Lozano-Perez, T., "A Framework for Multiple-Instance Learning," ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, pp. 570--576, 1998
2. Maron, O. and Ratan, A.L., "Multiple-Instance Learning for Natural Scene Classification," Proceedings of the Fifteenth International Conference on Machine Learning, pp. 341--349, 1998

12th February

Gyeongyong Heo

Fuzzy SVM for Noisy Data: A Robust Membership Calculation Method

5th February

Xuping Zhang

MCMC feature learning and classification.

21th January

Xuping Zhang

MCMC feature learning and classification.

15th January

Ganesh Ramachandran

GRANMA: Gradient Angle Model Algorithm on Wideband EMI data for Landmine Detection.

Date

Presenter

Paper

18th December

Seniha Esen Yuksel

Shihao Ji, B. Krishnapuram, L. Carin; "Variational Bayes for continuous hidden Markov models and its application to active learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, April 2006, Volume: 28, Issue: 4.

15th December

Ryan Busser

Using Gradient Ascent with a Confusion Matrix to Maximize the WMW Statistic

20th November

Alina Zare

PhD Dissertation Defense: Hyperspectral Endmember Detection and Band Selection using Bayesian Methods

20th November

Jeremy Bolton

PhD Dissertation Defense: Random Set Framework for Context-Based Classification

9th October

Ajit Rajwade

A. Rajwade, A. Banerjee, A. Rangarajan; "Probability Density Estimation using Isocontours and Isosurfaces: Application to Information Theoretic Image Registration," IEEE Transactions on Pattern Analysis and Machine Intelligence, to be published.

2nd October

Raazia Mazhar

Vidal, R.; Yi Ma; Sastry, S.; "Generalized principal component analysis (GPCA)," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.12, pp.1945-1959, Dec. 2005.

25th September

Raazia Mazhar

Vidal, R.; Yi Ma; Sastry, S.; "Generalized principal component analysis (GPCA)," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.12, pp.1945-1959, Dec. 2005.

18th September

Dr. Rolf Hummel

Dr. Rolf Hummel from the Materials Science and Engineering Department will be talking about his explosive detection research. Link to his research group

11th September

Gyeongyong Heo

"KG-FCM: Kernel-Based Global Fuzzy C-Means Clustering Algorithm," based on the following papers:
1. Aristidis Likas, Nikos Vlassis and Jakob J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, pp. 451-461, 2003
2. Weina Wang, Yunjie Zhang, Yi Li and Xiaona Zhang, "The Global Fuzzy C-Means Clustering Algorithm," Proceedings of the 6th World Congress on Intelligent Control, pp. 3604-3607, 2006, 2005
3. Zhong-dong Wu, Wei-xin Xie and Jian-ping Yu, "Fuzzy C-means clustering algorithm based on kernel method," Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications, pp.49-54, 2003

4th September

Alina Zare

"Variational inference" based on the following papers:
1. C.M. Bishop, Chapter 10: sApproximate Inference, Pattern Recognition and Machine Learning, Springer 2006.
2. D. M. Blei, and M. I. Jordan, "Variational methods for the Dirichlet process," Proceedings of the Twenty-First international Conference on Machine Learning, July 04 - 08, 2004

Date

Presenter

Paper

14th August

Seniha Esen Yuksel

Steve Waterhouse, David Mackay, Tony Robinson; "Bayesian Methods for Mixtures of Experts," Advances in Neural Information Processing Systems, 1996.

7th August

Seniha Esen Yuksel

S.R. Waterhouse, A.J. Robinson; "Classification using hierarchical mixtures of experts," Neural Networks for Signal Processing [1994] IV. Proceedings of the 1994 IEEE Workshop 6-8 Sept. 1994 Page(s):177 - 186

20th June

Raazia Mazhar

"Optimized Dictionary Design and Classification using The Matching Pursuits Based Dissimilarity Measure"

Date

Presenter

Paper

24th April

Seniha Esen Yuksel

Michael I. Jordan and Robert A. Jacobs, "Hierarchical Mixtures of Experts and the EM Algorithm," Massachusetts Institute of Technology, Artificial Intelligence Laboratory

18th April

Jeremy Bolton

"Random Set Model for Context-Based Classification," Thesis proposal.

17th April

Ryan Busser

V. C. Raykar, R. Duraiswami, B. Krishnapuram, "A fast algorithm for learning large scale preference relations," International Conference on Artificial Intelligence and Statistics (AISTATS), Puerto Rico, March 2007, vol. 2, pp. 388-395, March 2007

6th March

Fei Xu

"Simultaneous Inference and Database Sampling."

4th March

Gyeongyong Heo

"Spectral Graph Theory and Spectral Clustering," based on the following papers:
1. Ulrike von Luxburg, "A Tutorial on Spectral Clustering," Statistics and Computing 17(4), 2007
2. Igor Fischer and Jan Poland, "Amplifying the Block Matrix Structure for Spectral Clustering," Technical Report, 2005

28th February

Gyeongyong Heo

"Spectral Graph Theory and Spectral Clustering," based on the following papers:
1. Ulrike von Luxburg, "A Tutorial on Spectral Clustering," Statistics and Computing 17(4), 2007
2. Igor Fischer and Jan Poland, "Amplifying the Block Matrix Structure for Spectral Clustering," Technical Report, 2005

14th February

Jeremy Bolton

"Random Set Model for Context-Based Classification"

31st January

Raazia Mazhar

H. Frigui & R. Krishnapuram, "Clustering by Competitive Agglomeration," Pattern Recognition, vol. 30, no. 7, pp. 1109-1119, July 1997

17th January

Ganesan Ramachandran

X. Ma, D. Schonfeld, and A. Khokhar, "A General Two-Dimensional Hidden Markov Model and its Application in Image Classification," IEEE International Conference on Image Processing, San Antonio, Texas, 2007

Date

Presenter

Paper

27th November

Alina Zare

Teh, Y.W., Gorur, D. and Ghahramani, Z., "Stick-breaking Construction for the Indian Buffet," Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007), San Juan, Puerto Rico, 2007

20th November

Sudhir Rao

Finding Structure in Data: The central goal of unsupervised learning is to discover the underlying structure and patterns in the data. Since the type of structure varies with the data, we instead define a "goal" to extract those patterns which are useful in accomplishing it by constructing a global objective function. In doing so, we internally derive a teaching signal from the data itself through the principle of self organization. With the "goal" embedded in the cost function, these local forces define the rules of interaction between the data particles. Self organization of these particles should then reveal the structure in the data relevant to this goal. In this talk, I will be show how information theoretic learning principles can be used to derive these self organizing rules. A new information theoretic framework for unsupervised learning will be presented. In particular, the mean shift algorithms appear as special cases under this general framework giving them a whole new perspective. We will see how this framework could be applied to wide variety of applications ranging from clustering, principal curves to vector quantization.

13th November

Sean Matthews

Sato, M., Takahashi, K., Fujiwara, J., "Development of the Hand held dual sensor ALIS and its evaluation," 4th International Workshop on Advanced Ground Penetrating Radar 2007, 27-29 June 2007, Pages: 3-7

6th November

Seniha Esen Yuksel

R. Jenssen, K.E. Hild, , D. Erdogmus, J.C. Principe, T. Eltoft, "Clustering using Renyi's entropy," Image Proceedings of the International Joint Conference on Neural Networks, July 2003

30th October

Raazia Mazhar

Phillips, P.J., "Matching pursuit filters applied to face identification," Image Processing, IEEE Transactions on, vol. 7, no. 8, pp. 1150-1164, August 1998

16th October

Xuping Zhang

1. Petar M. Djuric, Joon-Hwa Chun, "An MCMC Sampling Approach to Estimation of Nonstationary Hidden Markov Models," IEEE Trans. Signal Process., vol. 50, no. 5, pp. 1113-1123, May 2002
2. Zoubin Ghahrammani, "An Introduction to Hidden Markov Models and Bayesian Networks," International Journal of Pattern Recognition and Artificial Intelligence 15(1):9-42
3. Olivier Cappe, Eric Moulines, Tobias Ryden, "Inference in Hidden Markov Models," Springer Book, 2005

9th October

Ryan Busser

Freund, Y., Iyer, R., Schapire, R.E., Singer, Y., "An Efficient Boosting Algorithm for Combining Preferences," Journal of Machine Learning Research, 4 (2003) 933-969, 2003

2nd October

Gyeongyong Heo

1. R. Krishnapuram and J.M. Keller, "A Possibilistic Approach to Clustering," IEEE Transactions on Fuzzy Systems 1(2), pp. 98-110
2. N.R. Pal, K. Pal, J.M. Keller and J.C. Bezdek, "A Possibilistic Fuzzy c-Means Clustering Algorithm," IEEE Transactions on Fuzzy Systems 13(4),pp. 517-530, 2005
3. D.E. Gustafson and W.C. Keller, "Fuzzy clustering with a fuzzy covariance matrix," Proceedings of the 1978 IEEE Conference on Decisionand Control, pp. 761-766, 1979
4. I. Gath and A.B. Geva, "Unsupervised Optimal Fuzzy Clustering," IEEE  Transactions on Pattern Analysis and Machine Intelligence 11(7), pp.773-781, 1989

25th September

Raazia Mazhar

1. Teh, Y.W., Jordan, M.I., Beal M.J. and Blei, D.M, "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, 2006
2. Teh, Y.W., Jordan, M.I., Beal M.J. and Blei, D.M, "Using Dependent Regions for Object Categorization in a Generative Framework," Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Volume 2, 2006 Page(s):1597 - 1604

18th September

Raazia Mazhar

Teh, Y.W., Jordan, M.I., Beal M.J. and Blei, D.M, "Using Dependent Regions for Object Categorization in a Generative Framework," Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Volume 2, 2006 Page(s):1597 - 1604

Date

Presenter

Paper

12th June

Andres Mendez-Vazquez

Dongxin Xu, Jose C. Principe, "Learning from Examples with Quadratic Mutual Information"

5th June

Ganesan Ramachandran

Dongxin Xu, Jose C. Principe, "Reject Option in Pattern Recognition : Overview and new Advances"

29th May

Alina Zare

1. S. Jain and R. M. Neal, "A Split-Merge Markov Chain Monte Carlo Procedure for Dirichlet Process Mixture Model"
2. A. Ranganathan, "The Dirichlet Process Mixture (DPM) Model"

22nd May

Alina Zare

1. S. Jain and R. M. Neal, "A Split-Merge Markov Chain Monte Carlo Procedure for Dirichlet Process Mixture Model"
2. A. Ranganathan, "The Dirichlet Process Mixture (DPM) Model"

8th May

Ganesan Ramachandran

1. Rathinavelu, C. Deng, L, "Use of Generalized Dynamic Feature Parameters for Speech Recognition"
2. Mario A.T. Figueiredo, "Adaptive Sparseness for Supervised Learning"

Date

Presenter

Paper

24th April

Alina Zare

J. Borges, J. Bioucas-Dias, and A. Marçal, "Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning," in Pattern Recognition and Image Analysis: 3rd Iberian Conference, IbPRIA 2007, Lecture Notes in Computer Science, Girona, Spain, 2007

17th April

Xuping Zhang

Jenssen, R., Eltoft, T., Girolami, M. and Erdogmus, D., "Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm"

9th April

Gyeongyong Heo

David J.C. MacKay, "Ensemble learning for hidden Markov models," Technical Report, 1997

27th March

Gyeongyong Heo

David J.C. MacKay, "Ensemble learning for hidden Markov models," Technical Report, 1997

20th March

Dr. Yijun Sun

Yijun Sun, "Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, June 2007

13th March

Jeremy Bolton

1. Rubner, Y. , Tomasi, C. and Guibas, L.J., "A Metric for Distributions with Applications to Image Databases"
2. Kwok-Leung Tam, Lau, R.W.H. and Chong-Wah Ngo, "Deformable geometry model matching by topological and geometric signatures," Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 23-26 Aug. 2004, Volume: 3, On page(s): 910- 913 Vol.3
3. Krahnstoever, N.; Sharma, R., "Robust probabilistic estimation of uncertain appearance for model-based tracking," Motion and Video Computing, 2002. Proceedings. Workshop on , vol., no., pp. 28-33, 5-6 Dec. 2002
4. Rubner, Y., Guibas, L. and Tomasi, C. "The Earth Mover's Distance, Multi-Dimensional Scaling, and. Color-Based Image Retrieval" Motion and Video Computing, 2002. Proceedings. Workshop on , vol., no., pp. 28-33, 5-6 Dec. 2002

6th March

Raazia Mazhar

Aahron M., Elad M., Bruckstein A., "K-SVD An Algorithm for Designing Overcomplete Dictionaries for Sparse represenation," IEEE transactions on Signal Processing, vol 54, no. 11, November 2006

12th February

Ganesan Ramachandran

Inoue, M., Ueda, N., "Exploitation of Unlabeled Sequences in Hidden Markov Models" Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 25, Issue 12, Dec. 2003 Page(s): 1570 - 1581

23rd January

Gyeongyong Heo

"Dirichlet Processes and its Extensions"

16th January

Gyeongyong Heo

"Dirichlet Processes and its Extensions"