Therefore, classifying cancer mi-croarray gene expression data can be regarded as a high-dimensional-low-sample data problem with lots of noisy or missing data.
The discovery booklet involved four parts: basic PASCAL programmes (with sample data), three problem statements (with input, output and the algorithmic process), pseudocode example and three problem statements.
In addition, the proposed method in this paper can be extendable to many famous dynamical systems, such as cellular NNs [8], state estimation problem [14, 15], filtering problem [48], impulsive problem [49, 50], Markovian jump NNs [34], and sampled data control problem [47].
However, the noise and incorrect sampling for sample data remain a problem [69].
My sense is that as learners are endowed with different kinds of learning algorithms, particularly ones that regard confirmation probabilistically and sample data statistically, the Unlearning problem disappears (or perhaps, disappears 'in the limit).' For more information and references, see the entry on formal learning theory.
Selection bias is typically considered to be a problem in sample data where certain types of individuals may be systematically included or excluded from a data set.
This paper focuses on the sampled-data synchronization problem of chaotic Lur'e systems (CLSs) by using sampled output of the systems with variable sampling rates.
The Pauta criterion [24] identifies the abnormal values of the sample data and is suitable to our problem.
Here we take a delay system approach to the H∞ sampled-data control problem.
This paper investigates the dissipative non-fragile output feedback sampled-data control problem for uncertain structural systems.
This paper addresses the dissipativity based resilient sampled-data control problem for a class of stochastic systems with actuator failures.
