A new evolutionary optimization technique.
A note on the richness of convex hulls of VC classes.
A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems
A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization...
A novel medical diagnosis system.
A primal sub-gradient method for structured classification with the averaged sum loss
A proximity based macro stress testing framework
In this a paper a non-linear macro stress testing methodology with focus on early warning is developed. The methodology builds on a variant of Random Forests and its proximity measures. It is embedded in a framework, in which naturally defined contagion and feedback effects transfer the impact of stressing a relatively small part of the observations on the whole dataset, allowing to estimate a stressed future state. It will be shown that contagion can be directly derived from the proximities while...
A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model...
A rough set-based knowledge discovery process
The knowledge discovery from real-life databases is a multi-phase process consisting of numerous steps, including attribute selection, discretization of real-valued attributes, and rule induction. In the paper, we discuss a rule discovery process that is based on rough set theory. The core of the process is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules from databases with uncertain and incomplete data....
A rule based machine learning approach to the nonlinear multifingered robot gripper problem
A scratch removal method
We present a new type of scratch removal algorithm based on a causal adaptive multidimensional prediction. The predictor use available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data but not any information from failed pixel itself. Predictor parameters cannot be directly identified so a special approximation is introduced.
A strategy learning model for autonomous agents based on classification
In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation...
A survey of factors influencing MLP error surface
A syntactic characterization of bounded-rank decision trees in terms of decision lists
About the maximum information and maximum likelihood principles
Neural networks with radial basis functions are considered, and the Shannon information in their output concerning input. The role of information- preserving input transformations is discussed when the network is specified by the maximum information principle and by the maximum likelihood principle. A transformation is found which simplifies the input structure in the sense that it minimizes the entropy in the class of all information-preserving transformations. Such transformation need not be unique...
About the use of neural networks in integrated numerical circuits and systems.
Adaptive control of system entropy
Algorithmes génétiques hybrides pour l'optimisation combinatoire
Algorithms for Classification based on k-NN.
An Application of Discriminant Analysis and Artificial Neural Networks to Classification Problems
An approach based on the use of the ant system to design combinational logic circuits.
In this paper we report the first attempt to design combinational logic circuits using the ant system. In order to design circuits, a measure of quality improvement in partially built circuits is introduced and a cost metric (based on the number of gates) is adopted in order to optimize the feasible circuits generated. The approach is compared to a genetic algorithm and to a human designer using several examples and the sensitivity of the algorithm to its parameters is studied using analysis of...