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This paper deals with classification algorithms as one of the basic principles of pattern recognition. We analyze their effect to a feature space and compare the type and the shape of the separating and decision surface, respectively. We proposed a novel classification approach based on Cumulative Fuzzy Membership Function that creates a decision surface in a different way as an MF ARTMAP neural network...
Visualization is one of the most powerful means for understanding the structure of multidimensional data. One of the most popular visualization methods is the Self-Organizing Map (SOM) that maps high dimensional data into low dimensional space while preserving the data's topological structure. While the topographical visualization can reveal the intrinsic characteristics of the data, SOM often fails...
This paper deals with an MF ARTMAP neural network. We study its behavior while training with different data sets and using different parameters. It gives us better knowledge of its strong and weak points. Subsequently, we focus on alleviation of weak points and improvement of strong points like the utilization of a one-shot learning, an incremental ability of the network without forgetting the already...
Kohonen’s self-organizing map (SOM) is used to map high-dimensional data into a low-dimensional representation (typically a 2-D or 3-D space) while preserving their topological characteristics. A major reason for its application is to be able to visualize data while preserving their relation in the high-dimensional input data space as much as possible. Here, we are seeking to go further by incorporating...
In this study we want to connect our previously proposed context-relevant topographical maps with the deep learning community. Our architecture is a classifier with hidden layers that are hierarchical two-dimensional topographical maps. These maps differ from the conventional self-organizing maps in that their organizations are influenced by the context of the data labels in a top-down manner. In...
In the last few years, many form of Learning Management Systems (LMS) have been introduced in many educational institutions with the main objective of obtaining meaningful information from the accumulated learning data to be then utilized for increasing the quality of the educations in those institutions. One of the most popular techniques for extracting information is by visualizing the high dimensional...
In this paper, we propose a hierarchical neural network similar to the Radial Basis Function (RBF) Network. The proposed Restricted RBF (rRBF) executes a neighborhood-restricted activation function for its hidden neurons and consequently generates a unique topological map, which differs from the conventional Self-Organizing Map, in its internal layer. The primary objective of this study is to visualize...
In this paper we report on our experiments in training an autonomous robot using a hierarchical neural network containing a topographical map in its hidden layer. The map topologically organizes the sensory information of the robot and propagates this information to the next layer that is trained in supervised manner. Through some physical experiments, we show that the order in the internal representation...
In this research we propose a trainable controller for a mobile robot based on a layered neural network, in which the hidden layer is a topographical map. In this study we focus not only on building a general controller that can be embedded to mobile robots running in physical environment, but also on building controllers with good internal plausibility. We consider that internal plausibility of the...
In this study an ensemble of several perceptrons with a simple competitive learning mechanism is proposed. The objective of this ensemble is to decompose a non-linear classification problem into several more manageable linear problems, thus realizing a piecewise-linear classifier. During the competitive learning process, each member of the ensemble competes to learn from one linear subproblem in a...
For decades human society has been supported by the proliferation of complex artifacts such as electronic appliances, personal vehicles and mass transportation systems, electrical and communications grids, and in the past few decades, Internet. In the very near future, robots will play increasingly important roles in our daily life. The increase in complexity of the tasks and sometimes physical forms...
In this study, we physically built hardware modules which enable us to freely construct robots with various morphologies. As opposed to the existing studies of modular robotics where the connection topology among the modules has to be hand-designed, our modules are able to adaptively modify their connection topology which enables them to generate an overall behavior as one robot. We ran several physical...
Models of self-organizing cortical maps have focused on demonstrations with single objects in the environment. Recently, the validity of a traditional biological model has been questioned for the case of multiple simultaneous input sources. Here we show that the standard model is able to self-organize with multiple inputs. However, we also show that the ability to self-organization can be enhanced...
In this paper we analyze the performance of a neural network ensemble in performing piecewise linear classification by automatically decomposing a non-linear problem into several linear sub-problems. The strength and weakness of this neural network ensemble with respect to MLP and Perceptron, and the diversity in the ensemble?s modules, created as the result of the competitive learning process, are...
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