Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method

Multi-instance multi-label learning (MIML) is an innovative learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the multi-instance multi-label RBF neural networks (MIMLRBF) can exploit connections between the instances and the labels of an MIML example directly, while most of other algorithms cannot learn that directly. However, the singular value decomposition (SVD) method used to compute the weights of the output layer will cause augmented overall error in network performance when training data are noisy or not easily discernible. This paper presents an improved approach to learning algorithms used for training MIMLRBF. The steepest descent (SD) method is used to optimize the weights after they are initialized by the SVD method. Comparing results employing diverse learning strategies shows interesting outcomes as have come out of this paper.

[1]  Reza Sabzevari,et al.  Three-phase strategy for the OSD learning method in RBF neural networks , 2009, Neurocomputing.

[2]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[3]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[4]  Thomas Gärtner,et al.  Multi-Instance Kernels , 2002, ICML.

[5]  Zhi-Hua Zhou,et al.  MIML: A Framework for Learning with Ambiguous Objects , 2008, ArXiv.

[6]  Shan Xiaohong.  Optimization Approach Based on Genetic Algorithm for RBF Neural Network , 2007 .

[7]  Zhi-Hua Zhou,et al.  Multi-instance clustering with applications to multi-instance prediction , 2009, Applied Intelligence.

[8]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[9]  G. A. Edgar.  Measure, Topology, and Fractal Geometry , 1990 .

[10]  ZhouZhi-Hua,et al.  Multi-instance clustering with applications to multi-instance prediction , 2009 .

[11]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[12]  Licheng Jiao,et al.  Decentralized backstepping output-feedback control for stochastic interconnected systems with time-varying delays using neural networks , 2011, Neural Computing and Applications.

[13]  Sebastián Ventura,et al.  Multiple instance learning for classifying students in learning management systems , 2011, Expert Syst. Appl..

[14]  Thomas Hofmann,et al.  Kernel Methods for Missing Variables , 2005, AISTATS.

[15]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[16]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[17]  Thomas Hofmann,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2007 .

[18]  J. E. Kelley,et al.  The Cutting-Plane Method for Solving Convex Programs , 1960 .

[19]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[20]  Licheng Jiao,et al.  Adaptive Tracking for Periodically Time-Varying and Nonlinearly Parameterized Systems Using Multilayer Neural Networks , 2010, IEEE Transactions on Neural Networks.

[21]  Nathan Intrator,et al.  Global Optimization of RBF Networks , 2000 .

[22]  Min-Ling Zhang,et al.  MIMLRBF: RBF neural networks for multi-instance multi-label learning , 2009, Neurocomputing.

[23]  Zhi-Hua Zhou,et al.  M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning , 2008, 2008 Eighth IEEE International Conference on Data Mining.