Abstract:
It is shown that frequency sensitive competitive learning (FSCL), one version of the recently improved competitive learning (CL) algorithms, significantly deteriorates in...Show MoreMetadata
Abstract:
It is shown that frequency sensitive competitive learning (FSCL), one version of the recently improved competitive learning (CL) algorithms, significantly deteriorates in performance when the number of units is inappropriately selected. An algorithm called rival penalized competitive learning (RPCL) is proposed. In this algorithm, not only is the winner unit modified to adapt to the input for each input, but its rival (the 2nd winner) is delearned by a smaller learning rate. RPCL can be regarded as an unsupervised extension of Kohonen's supervised LVQ2. RPCL has the ability to automatically allocate an appropriate number of units for an input data set. The experimental results show that RPCL outperforms FSCL when used for unsupervised classification, for training a radial basis function (RBF) network, and for curve detection in digital images.<>
Published in: IEEE Transactions on Neural Networks ( Volume: 4, Issue: 4, July 1993)
DOI: 10.1109/72.238318
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- IEEE Keywords
- Index Terms
- Radial Basis Function ,
- Competitive Learning ,
- Learning Algorithms ,
- Learning Rate ,
- Digital Images ,
- Supervised Learning ,
- Unsupervised Learning ,
- K-means Algorithm ,
- Crucial Problem ,
- Vector Quantization ,
- Radial Basis Function Network ,
- Small Learning Rate ,
- Training Set ,
- Learning Process ,
- Image Pixels ,
- Learning Curve ,
- Data Clustering ,
- Weight Vector ,
- Linear Unit ,
- Extra Unit ,
- Cluster Centers ,
- Recognition Rate ,
- Units In Layer ,
- Line Segment ,
- Output Units ,
- Hough Transform
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Radial Basis Function ,
- Competitive Learning ,
- Learning Algorithms ,
- Learning Rate ,
- Digital Images ,
- Supervised Learning ,
- Unsupervised Learning ,
- K-means Algorithm ,
- Crucial Problem ,
- Vector Quantization ,
- Radial Basis Function Network ,
- Small Learning Rate ,
- Training Set ,
- Learning Process ,
- Image Pixels ,
- Learning Curve ,
- Data Clustering ,
- Weight Vector ,
- Linear Unit ,
- Extra Unit ,
- Cluster Centers ,
- Recognition Rate ,
- Units In Layer ,
- Line Segment ,
- Output Units ,
- Hough Transform