Titlebook: Machine Learning and Data Mining in Pattern Recognition; 5th International Co Petra Perner Conference proceedings 2007 Springer-Verlag Berl

conduct

An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectorselection procedures are of vital importance to machine learning and data mining. The suggested approach is based on the multiple agent paradigm. The authors propose using JABAT middleware as a tool and the original instance reduction procedure as a method for selecting reference vectors under multip

啪心兒跳動(dòng)

On Applying Dimension Reduction for Multi-labeled Problems problem such as text categorization, data samples can belong to multiple classes and the task is to output a set of class labels associated with new unseen data sample. As common in text categorization problem, learning a classifier in a high dimensional space can be difficult, known as the curse o

Kidnap

Nonlinear Feature Selection by Relevance Feature Vector Machinenear dependencies between features. However it is known that the number of support vectors required in SVM typically grows linearly with the size of the training data set. Such a limitation of SVM becomes more critical when we need to select a small subset of relevant features from a very large numb

Manifest

Affine Feature Extraction: A Generalization of the Fukunaga-Koontz Transformations principal component analysis (.), Fisher’s linear discriminant analysis (.), et al. In this paper, we describe a novel feature extraction method for binary classification problems. Instead of finding linear subspaces, our method finds lower- dimensional affine subspaces for data observations. Our

TIA742

關(guān)心

JOG

Kernel MDL to Determine the Number of Clusters Kernel MDL (KMDL), is particularly adapted to the use of kernel K-means clustering algorithm. Its formulation is based on the definition of MDL derived for Gaussian Mixture Model (GMM). We demonstrate the efficiency of our approach on both synthetic data and real data such as SPOT5 satellite images

Obstreperous

Critical Scale for Unsupervised Cluster Discoverya points based on the estimation of probability density function (PDF) using a Gaussian kernel with a variable scale parameter. It has been suggested that the detected cluster, represented as a mode of the PDF, can be validated by observing the lifetime of the mode in scale space. Statistical proper

Fulsome

在前面

A Clustering Algorithm Based on Generalized Starsithm proposed by Aslam ., and recently improved by them and other researchers. In this method we introduced a new concept of star allowing a different star-shaped form with better overlapping clusters. The evaluation experiments on standard document collections show that the proposed algorithm outpe