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Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion...
An improved classified image interpolation algorithm is presented. The algorithm obtains high-resolution pixels by filtering with parameters that are optimal for the selected class. By applying the highly flexible neural network model in the proposed algorithms, classified image data is extended into a nonlinear model in each class while enhancing the sharpness and edge characteristic. Meantime the...
We solve Nash equilibrium of stochastic games using heuristic Q-learning method based on ldquoheuristic learningrdquo + ldquo Q-learningrdquo under the framework of noncooperative general-sum games. Determining whether a strategy Nash equilibrium exists in a stochastic game is NP-hard even if the game is finite. Therefore normal Q-learning method based on iterative learning canpsilat solve stochastic...
Based on the analysis and comparisons of complexity between stochastic segment model (SSM) and hidden Markov model (HMM) in this paper, we presented a fast and robust SSM, which yields a 94.75% speaker-independent performance on Mandarin digit string recognition. This result is better than HMM based system at the same level of computational complexity and just only a little slower than HMM in the...
The Intrusion detection system (IDS) is a security technology that attempts to identify network intrusions. Defending against multistep intrusions which prepare for each other is a challenging task. In this paper, the Context-Free Grammar (CFG) was used to describe the multistep attacks using alerts classes. Based on the CFGs, the modified LR parser was employed to generate the parse trees of the...
Quantum computation algorithms indicate possibility that non-deterministic polynomial time (NP-time) problems can be solved much faster than by classical methods. Farhi et al., have proposed an adiabatic quantum computation (AQC) for solving the three-satisfiability problem (3-SAT). We have proposed a neuromorphic quantum computation algorithm based on AQC, in which an analogy to an artificial neural...
In the field of digital speech recognition powerful ASR (automatic speech recognizer) systems have been developed which employ highly intricate algorithms like the HMM, DTW and neural network based algorithms capable of recognizing up to 1000 different words. Their high complexity and computation requirements prove to be superfluous for less demanding tasks. In this paper is proposed a simple, less...
Outlier detection has received considerable attention in many applications, such as detecting network attacks or credit card fraud The massive datasets currently available for mining in some of these outlier detection applications require large parallel systems, and consequently parallelizable outlier detection methods. Most existing outlier detection methods assume that all of the attributes of a...
The choice of network dimension is a fundamental issue in neural network applications. An optimal neural network topology not only reduces the computational complexity, but also improves its generalization capacity. In this research, a new pruning algorithm based on cross validation and sensitivity analysis is developed and compared with three existing pruning algorithms on various pattern classification...
In this paper, we propose a model to develop robotspsila covert and overt behaviors by using reinforcement and supervised learning jointly. The covert behaviors are handled by a motivational system, which is achieved through reinforcement learning. The overt behaviors are directly selected by imposing supervised signals. Instead of dealing with problems in controlled environments with a low-dimensional...
Kernel ridge regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter sigma. We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian process. Since...
In this paper, a neural network (NN) for peak power reduction of orthogonal frequency-division multiplexing (OFDM) signals is improved in order to suppress its computational complexity. Numerical experiments show that the proposed NN has less computational complexity than the conventional one. The number of IFFT in NN can be reduced to half, and the computational time can be suppressed by 32.7%. From...
Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more...
A new sparse kernel model for spectral clustering is presented. This method is based on the incomplete Cholesky decomposition and can be used to efficiently solve large-scale spectral clustering problems. The formulation arises from a weighted kernel principal component analysis (PCA) interpretation of spectral clustering. The interpretation is within a constrained optimization framework with primal...
Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Meta-learning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored...
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