Add to ORKGMachines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. This book looks at data complexity and its role in shaping the theories and techniques in different discipline
We conducted comparative analysis of different supervised dimension reduction techniques by integrat...
We design several algorithms representing evaluation processes of different complexity, ranging from...
Doctor of PhilosophyComputational complexity in data mining is attributed to algorithms but lies hug...
Abstract. We investigate the role of data complexity in the context of binary classification problem...
It is widely accepted that the empirical behavior of classifiers strongly depends on available data....
We investigate the role of data complexity in the context of binary classification problems. The uni...
We discuss basic sample complexity theory and it's impact on classification success evaluation,...
In perception research, various models have been designed for the encoding of, for example, visual p...
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in...
Abstract Most data complexity studies have focused on characterizing the complexity of the entire da...
Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an outp...
Abstract—This paper examines fundamental problems underlying difficulties encountered by pattern rec...
Complexity is your problem, classifiers may offer a solution. These rule-based, multifaceted, machin...
Item does not contain fulltextResearch on pattern perception and rule learning, grounded in formal l...
Classification complexity estimation is one of the fundamental steps in pattern recognition in order...
We conducted comparative analysis of different supervised dimension reduction techniques by integrat...
We design several algorithms representing evaluation processes of different complexity, ranging from...
Doctor of PhilosophyComputational complexity in data mining is attributed to algorithms but lies hug...
Abstract. We investigate the role of data complexity in the context of binary classification problem...
It is widely accepted that the empirical behavior of classifiers strongly depends on available data....
We investigate the role of data complexity in the context of binary classification problems. The uni...
We discuss basic sample complexity theory and it's impact on classification success evaluation,...
In perception research, various models have been designed for the encoding of, for example, visual p...
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in...
Abstract Most data complexity studies have focused on characterizing the complexity of the entire da...
Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an outp...
Abstract—This paper examines fundamental problems underlying difficulties encountered by pattern rec...
Complexity is your problem, classifiers may offer a solution. These rule-based, multifaceted, machin...
Item does not contain fulltextResearch on pattern perception and rule learning, grounded in formal l...
Classification complexity estimation is one of the fundamental steps in pattern recognition in order...
We conducted comparative analysis of different supervised dimension reduction techniques by integrat...
We design several algorithms representing evaluation processes of different complexity, ranging from...
Doctor of PhilosophyComputational complexity in data mining is attributed to algorithms but lies hug...