The curse of dimensionality is a problem of machine learning algorithm which is often encountered on study of high-dimensional data, while LSDA (Locality Sensitive Discriminant Analysis) can solve the problem of curse of dimensionality. However, LSDA can not fully reflect the requirements that the manifold learning for neighborhood, by using the adaptive neighborhood selection method to measure...

This paper develops the Bayesian model selection based on Bayes factor for a rich class of partially-observed micro-movement models of asset price. We focus on one recursive algorithm to calculate the Bayes factors, first deriving the system of SDEs for them and then applying the Markov chain approximation method to yield a recursive algorithm. We prove the consistency (or robustness) of the re...

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated 17.5 million people died from cardiovascular disease, while 2008, HF disease represented 31% of patient deaths worldwide. One the new breakthroughs for early diagnosis utilizing data mining techniques. this study, Correlate...

The learning curve of Bayes optimal classii-cation algorithm when learning a perceptron from noisy random training examples is calculated exactly in the limit of large training sample size and large instance space dimension using methods of statistical mechanics. It is shown that under certain assumptions, in this \thermodynamic" limit, the probability of misclassiication of Bayes optimal algor...

Medical data sets consist of a huge amount of data organized in instances, where each one contains several attributes. The quality of the models obtained from a database strongly depends on the information previously stored on it. For this reason, these data sets must be preprocessed in order to have fairly information about patients. Data sets are preprocessed reducing the amount of data. For ...

The NP-hard capacitated clustering problem (CCP) is a general model with a number of relevant applications. This paper proposes a highly effective iterated variable neighborhood search (IVNS) algorithm for solving the problem. IVNS combines an extended variable neighborhood descent method and a randomized shake procedure to explore effectively the search space. The computational results obtaine...

Patients with liver disease have been continuously increasing because of excessive consumption of alcohol, inhalation of harmful gases, intake of contaminated food, pickles and drugs. Automatic classification tools may reduce burden on doctors. This paper evaluates the selected classification algorithms for the classification of some liver patient datasets. Classification algorithms considered ...

Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. This paper presents a variant of the Naive Bayes method, in which the original training set is augmented in the following fashion: Leave-One-Out procedure is applied over the training set, and incorrectly classified i...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...