Machine Learning Basics

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Papers in this group

1. Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships.

   Li Liao, William Stafford Noble in Journal of computational biology : a journal of computational molecular cell biology (2003)

   One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated…

   Added 2012-04-12 31 readers
2. A Bayesian Approach to Unsupervised Semantic Role Induction

   A Klementiev in Proceedings of the Conference of the European (2012)

   Added 2012-03-19 1 reader
3. Support vector machines in DSC-based glioma imaging: suggestions for optimal characterization.

   Frank G Zöllner, Kyrre E Emblem, Lothar R Schad in Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine (2010)

   Dynamic susceptibility contrast magnetic resonance perfusion imaging (DSC-MRI) is a useful method to characterize gliomas. Recently, support vector machines (SVMs) have been introduced as means to prospectively characterize new patients based on…

   Added 2012-03-17 54 readers
4. Predictive modeling in glioma grading from MR perfusion images using support vector machines.

   Kyrre E Emblem, Frank G Zoellner, Bjorn Tennoe, Baard Nedregaard, Terje Nome, Paulina Due-Tonnessen, et al. in Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine (2008)

   The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood…

   Added 2012-03-17 9 readers
5. Classification of Segmented Regions in Brightfield Microscope Images

   Marko Tscherepanow, Franz Kummert Frank G. Zöllner

   Added 2012-03-17 4 readers
6. Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction

   Habib Shah, Rozaida Ghazali, Nazri Mohd Nawi in Journal of Computing (2011)

   Added 2012-03-08 3 readers
7. Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network

   Rozaida Ghazali, Abir Jaafar Hussain, Nazri Mohd Nawi, Baharuddin Mohamad in Neurocomputing (2009)

   Added 2012-03-08 3 readers
8. Incomplete Information of Decision Support System for Planting Material Selection

   Mohd Najib B. Mohd Salleh, Nazri Mohd Nawi, Patrice Boursier in International Conference on Networked Computing and Advanced Information Management( NCM 2009) (2009)

   Added 2012-03-08 1 reader
9. An Improved Learning Algorithm Based on The Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method For Back Propagation Neural Networks

   Nazri Mohd Nawi, Meghana R. Ransing, Rajesh S. Ransing in Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA 2006) (2006)

   Added 2012-03-08 2 readers
10. A study on traditional Malay musical instruments sounds classification system

    Norhalina Senan, Rosziati Ibrahim, Nazri Mohd Nawi in iiWAS'2009 - The Eleventh International Conference on Information Integration and Web-based Applications and Services (2009)

    Added 2012-03-08 1 reader
11. A New Method to Improve the Gradient Based Search Direction to Enhance the Computational Efficiency of Back Propagation Based Neural Network Algorithms

    Nazri Mohd Nawi, Rajesh S. Ransing, Meghana R. Ransing in Second Asia International Conference on Modelling and Simulation (AMS 2008) (2008)

    Added 2012-03-08 2 readers
12. Incremental learning with support vector machines

    S. Ruping in Data Mining, 2001. ICDM 2001, Proceedings IEEE (2001)

    Added 2012-03-01
13. Feature selection for SVMs

    J. Weston, S. Mukherjee, O Chapelle, M. Pontil, T. Poggio, V. Vapnik in Advances in neural information processing systems (2001)

    Added 2012-03-01 4 readers
14. Applying support vector machines to imbalanced datasets

    Rehan Akbani, Stephen Kwek, Nathalie Japkowicz in Machine Learning: ECML 2004 (2004)

    Added 2012-03-01 30 readers
15. An Introduction to Variable and Feature Selection

    Isabelle Guyon in Journal of Machine Learning Research (2003)

    Added 2012-03-01 649 readers
16. A learning method for the class imbalance problem with medical data sets.

    Der-Chiang Li, Chiao-Wen Liu, Susan C Hu in Computers in biology and medicine (2010)

    In medical data sets, data are predominately composed of "normal" samples with only a small percentage of "abnormal" ones, leading to the so-called class imbalance problems. In class imbalance problems, inputting all the data into the classifier to…

    Added 2012-03-01 6 readers
17. A learning method for the class imbalance problem with medical data sets.

    Der-Chiang Li, Chiao-Wen Liu, Susan C Hu in Computers in biology and medicine (2010)

    In medical data sets, data are predominately composed of "normal" samples with only a small percentage of "abnormal" ones, leading to the so-called class imbalance problems. In class imbalance problems, inputting all the data into the classifier to…

    Added 2012-03-01 6 readers
18. Random Forests

    Leo Breiman in Statistics (2001)

    Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to…

    Added 2012-02-28 196 readers