Working Papers

• Zaidi, N. and Webb, G. and Petitjean, F. and Forestier, G.
  On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error
  (in submission to DMKD) (2018)
  [pdf][code]

• Petitjean, F. and Buntine, W. and Webb, G. and Zaidi, N.
  Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
  (conditionally accepted) Machine Learning Journal (MLj), 2018
  [pdf]

• Zaidi, N. and 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]

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, Issue 9-10, pp. 1289-1329 (2017)
  [doi][pre-publication pdf] [code]

• Zaidi, N. and Webb, G. and Carman, M. and Petitjean, F. and Cerquides, J.
  ALRⁿ: 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 Petitjean, F. and Webb, G.
  Efficient and Effective Accelerated Higher-order Logistic Regression for Large Data Quantities
  SDM2018: SIAM International Conference on Data Mining, (2018)
  [pdf]
• Zaidi, N. and Webb, G.
  A Fast Trust-Region Newton Method for Softmax Logistic Regression
  SDM2017: SIAM International Conference on Data Mining, (2017)
  [pdf] [Slides]
• 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]