The storage capacity of an incremental learning algorithm for the parity machine, the Tilinglike Learning Algorithm, is analytically determined in the limit of a large number of hidden perceptrons. Different learning rules for the simple perceptron are investigated. The usual Gardner-Derrida one leads to a storage capacity close to the upper bound, which is independent of the learning algorithm...

The purpose of this study is to find the relationships between personal demographic attributes and long distance travel mode choices based on the Artificial Intelligence technique-rough set theory. Rough set theory can learn and refine decision rules or hidden facts from the incomplete observed data without the constraints of statistical assumptions. Also the induced decision rules are expresse...

Data mining process is used to extract knowledge from the database. Large numbers of data mining tools are available to get the useful information. These tools can be utilized to break the privacy and security of useful sensitive information present in the database. This sensitive information may be personal information, patterns, facts etc. This sensitive information if mined will result in lo...

Data mining methodology has a tremendous contribution for extracting the hidden knowledge and patterns from the existing databases. Traditionally, researchers use basket data to mine association rules of which the basic task is to find the frequent items. For relational databases whose data format is relational data other than basket data, RDB-MINER algorithm was proposed. In this paper, we int...

This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antece...

Classiication is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classiication rules using neural networks. Neural networks have not been thought suited for data mining because how the classiications were made is not explicitly stated as symbolic rules that are suitable for veriication or interpre...

This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Each has innate strengths and weaknesses; the combination results in a very high precision tagger. MaxEnt includes external gazetteers in the system. Sub-category generation is also discussed.

A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models, and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calcula...

In this article, we assume that the mesons in the πχc1 invariant mass distribution are scalar hidden charmed tetraquark states and calculate their masses with the QCD sum rules. The numerical result indicates that the masses are about MZ = (4.48 ± 0.12)GeV. In previous work, we observe that the masses of the vector hidden charmed tetraquark states are about MZ = (4.81±0.11)GeV or MZ = (4.83±0.1...

Learning and inferring features that generate sensory input is a task continuously performed by cortex. In recent years, novel algorithms and learning rules have been proposed that allow neural network models to learn such features from natural images, written text, audio signals, etc. These networks usually involve deep architectures with many layers of hidden neurons. Here we review recent ad...