Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines.
Walker H. LandDaniel W. McKeeFrances R. AndersonJoseph Y. LoPublished in: Medical Imaging: Image Processing (2004)
Keyphrases
- kernel function
- support vector
- breast cancer
- logistic regression
- evolutionary programming
- breast cancer diagnosis
- support vector machine
- large margin classifiers
- bladder cancer
- svm classifier
- svm classification
- kernel methods
- feature space
- classification accuracy
- kernel parameters
- mammogram images
- rbf kernel
- polynomial kernels
- kernel learning
- artificial immune system
- loss function
- microcalcification clusters
- radial basis function
- cross validation
- feature selection
- feature set
- support vector machine svm
- breast cancer patients
- decision trees
- data mining
- data sets
- training data
- breast tissue
- high dimensional
- feature vectors
- maximum margin
- multiple kernel learning
- step size
- artificial neural networks
- computational intelligence
- pairwise
- simulated annealing
- cancer patients
- feature extraction
- global optimization
- particle swarm optimization