## Working Papers

- Zaidi, N. Du, Y. and Webb, G.

**On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers**

(in submission to) Machine Learning Journal (MLj) (2017)

[pdf][code]

- Zaidi, N. and Webb, G. and Petitjean, F. and Buntine, W.

**FewPla: Efficient and Effective Limited Pass Learning for Large Data Quantities**

(in submission) (2017)

[pdf][code]

## Journal Papers

- Zaidi, N. and Webb, G. and Carman, M. and Petitjean, F. and Buntine, W. and Hynes, M. and De Sterck, H.

**Efficient Parameter Learning of Bayesian Network Classifiers**

Machine Learning, Volume 106, pp. 1-44

[doi][pre-publication pdf] [code]

- Zaidi, N. and Webb, G. and Carman, M. and Petitjean, F. and Cerquides, J.

**ALR**^{n}: Accelerated Higher-Order Logistic Regression

Machine Learning, Volume 104, Issue 2, pp. 151-194 (2016)

[doi] [pre-publication pdf] [code] [Slides] [Errata] - Martinez, A. and Webb, G. and Chen, S. and Zaidi, N.

**Scalable Learning of Bayesian Network Classifiers**

Journal of Machine Learning Research, 17, pp. 1-35 (2016)

[pdf] - Zaidi, N. and Cerquides, J. and Carman, M. and Webb, G.

**Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting**Journal of Machine Learning Research, 14, pp. 1947-1988 (2013)

[pdf] [code]

## Conference Papers

- Zaidi, N. and Webb, G.
**A Fast Trust-Region Newton Method for Softmax Logistic Regression**

SDM2017: SIAM International Conference on Data Mining, (2017)

[pdf] - Liu, N. and Zaidi, N.
**Artificial Neural Network: Deep or Broad? An Empirical Study**

AI2016: Advances in Artificial Intelligence, (2016)

[pdf] [Slides] - Zaidi, N. and Petitjean, F. and Webb, G.

**Preconditioning an Artificial Neural Network Using Naive Bayes**Advances in Knowledge Discovery and Data Mining, pp. 341-353 (2016)

[doi] [pre-publication pdf] [slides] - Zaidi, N. and Carman, M. and Cerquides, J. and Webb, G.

**Naive-Bayes Inspired Effective Pre-Conditioners for Speeding-up Logistic Regression**IEEE International Conference on Data Mining, pp. 1097-1102 (2014)

[doi] [pre-publication pdf] - Zaidi, N. and Webb, G.

**Fast and Efficient Single Pass Bayesian Learning**

Advances in Knowledge Discovery and Data Mining, pp. 149-160 (2012)

[doi] [pdf] [slides] - Zaidi, N. and Squire, D.

**Local Adaptive SVM for Object Recognition**

Digital Image Computing: Techniques and Applications (DICTA), pp. 196-201 (2010)

[doi] [pdf] [slides] - Zaidi, N. and Squire, D. and Suter, D.

**A Gradient-based Metric Learning Algorithm for k-NN Classifiers**

AI 2010: Advances in Artificial Intelligence, pp. 194-203 (2010)

[doi] [pdf] [slides] - Zaidi, N. and Squire, D.

**SVMs and Data Dependent Distance Metric**

Image and Vision Computing New Zealand (IVCNZ), pp. 1-7 (2010)

[doi] [pdf] - Zaidi, N. and Squire, D. and Suter, D.

**BoostML: An Adaptive Metric Learning for Nearest Neighbor Classification**

Advances in Knowledge Discovery and Data Mining, pp. 142-149 (2010)

[doi] [pdf] - Dowe, D. and Zaidi, N.

**Database Normalization as a by-product of Minimum Message Length inference**

AI 2010: Advances in Artificial Intelligence, pp. 82-91 (2010)

[doi] [pdf] - Zaidi, N. and Suter, D.

**Confidence rated boosting algorithm for generic object detection**

Pattern Recognition, 2008. ICPR 2008. 19th International Conference, pp. 1-4 (2008)

[doi] - Zaidi, N. and Suter, D.

**Object Detection Using a Cascade of Classifiers**

Digital Image Computing: Techniques and Applications (DICTA), pp. 600-605 (2008)

[doi]

## Technical Reports

- Zaidi, N and Squire, D and Suter, D.

**A Simple Gradient-based Metric Learning Algorithm for Object Recognition**

Technical Report (2010/256), Clayton School of IT, Monash University, VIC, Australia, 2010

[pdf] - Zaidi, N and Squire. D.

**Data Dependent Distance Metric for Efficient Gaussian Process Classification**

Technical Report, Clayton School of IT, Monash University, VIC, Australia, 2009

[pdf]

## Thesis

- Zaidi, N.

**Metric Learning and Scale Estimation in High Dimensional Machine Learning Problems with an Application to Generic Object Recognition**

Ph.D Thesis, 2011

[pdf]