In this paper, we propose and describe efficient multiclass classification and recognition of unconstrained handwritten Arabic words using machine learning approaches which include the K-nearest neighbor (K-NN) clustering, and the neural network (NN). The technical details are presented in terms of three stages, namely preprocessing, feature extraction and classification. Firstly, words are seg...

Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. In the hybrid system, NN is used for character level recognition while HMM is used for producing word score based on the probability of the hypothesized characters combined. All reported results shows better recognition for the hybrid system due to better disc...

The long-standing problem of efficient nearest-neighbor (NN) search has ubiquitous applications ranging from astrophysics to MP3 fingerprinting to bioinformatics to movie recommendations. As the dimensionality of the dataset increases, exact NN search becomes computationally prohibitive; (1+ ) distance-approximate NN search can provide large speedups but risks losing the meaning of NN search pr...

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present “HDIdx”, an efficient high-dimensional indexing library for f...

Ishihara’s Boundedness Principle BD-N was introduced in [Ish92] and has turned out to be most useful for constructive analysis, see e.g. [Ish01]. It is equivalent to the statement that every sequentially continuous function from NN to N is continuous w.r.t. the usual metric topology on NN. We construct models for higher order arithmetic and intuitionistic set theory in which both every function...

The nearest neighbour (NN) rule is widely used in pattern recognition tasks due to its simplicity and its good behaviour. Many fast NN search algorithms have been developed during last years. However, in some classification tasks an exact NN search is too slow, and a way to quicken the search is required. To face these tasks it is possible to use approximate NN search, which usually increases e...

SUMMARY Using the famous airline data as the main example, a variety of neural network (NN) models are tted and the resulting forecasts are compared with those obtained from the (Box-Jenkins) seasonal ARIMA model called the airline model. The results suggest that there is plenty of scope for going badly wrong with NN models and that it is unwise to apply them blindly in`black-box' mode. Rather ...

This paper proposes a novel approach based on the training of the Neural Network method with Particle Swarm Optimization (PSO-NN) for identification of a hydraulic servo robot. The robot is considered to have two degrees of freedom; one is rotational and the other is translational. A feed forward NN is designed for the problem and the weights of the network are trained using Particle Swarm Opti...

One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks (NN). EC is used to discover appropriate control actions for specific plant states. The survivors of the evolutionary process ...

The sensitivity of nucleon-nucleus elastic scattering observables to the offshell structure of nucleon-nucleon t-matrices, derived from realistic NN potentials, is investigated within the context of a full-folding model based on the impulse approximation. Our study uses recently developed NN potential models, which describe a subset of the NN data base with a χ2 per datum ∼1, which means that t...