Above are papers related to the following paper: [Paper_ID] Paper #42848. 	[Title] Evolving Fuzzy Prototypes for Efficient Data Clustering [Abstract] number of prototypes used to represent each class, the position of each prototype within its class and the membership function associated with each prototype. This paper proposes a novel, evolutionary approach to data clustering and classification which overcomes many of the limitations of traditional systems. The approach rests on the optimisation of both the number and positions of fuzzy prototypes using a real-valued genetic algorithm (GA). Because the GA acts on all of the classes at once, the system benefits naturally from global information about possible class interactions. In addition, the concept of a receptive field for each prototype is used to replace the classical distance-based membership function by an infinite fuzzy support, multidimensional, Gaussian function centred over the prototype and with unique variance in each dimension, reflecting the tightness of the cluster. Hence, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. Most data clustering algorithms, including the popular K-means algorithm, require a priori knowledge about the problem domain to fix the number and starting positions of the prototypes. Although such knowledge may be assumed for domains whose dimensionality is fairly small or whose underlying structure is relatively intuitive, it is clearly much less accessible in hyper-dimensional settings, where the number of input parameters may be very large. Classical systems also suffer from the fact that they can only define clusters for one class at a time. Hence, no account is made of potential interactions among classes. These drawbacks are further compounded by the fact that the ensuing classification is typically based on a fixed, distance-based membership function for all prototypes. This paper proposes a novel approach to data clustering and classification which overcomes the aforementioned limitations of traditional systems. The model is based on the genetic evolution of fuzzy prototypes. A real-valued genetic algorithm (GA) is used to optimise both the number and positions of prototypes. Because the GA acts on all of the classes at once and measures fitness as classification accuracy, the system naturally profits from global information about class interaction. The concept of a receptive field for each prototype is also presented and used to replace the classical, fixed distance-based function by an infinite fuzzy support membership function. The new membership function is inspired by that used in the hidden layer of RBF networks. It consists of a multidimensional Gaussian function centred over the prototype and with a unique variance in each dimension that reflects the tightness of the cluster. During classification, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116835. 	[Title] SUPERVISED COMPETITIVE LEARNING FOR FINDING POSITIONS OF RADIAL BASIS FUNCTIONS [Abstract] This paper introduces the magnetic neural gas (MNG) algorithm, which extends unsupervised competitive learning with class information to improve the positioning of radial basis functions. The basic idea of MNG is to discover heterogeneous clusters (i.e., clusters with data from different classes) and to migrate additional neurons towards them. The discovery is effected by a heterogeneity coefficient associated with each neuron and the migration is guided by introducing a kind of magnetic effect. The performance of MNG is tested on a number of data sets, including the thyroid data set. Results demonstrate promise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #910. 	[Title] Learning networks for face analysis and synthesis [Abstract] This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for the problem of face detection, "near miss" faces as well) with descriptive parameters for pose, expression, identity, and face vs. non-face. Then, by using example-based learning techniques, we develop networks for performing analysis tasks such as pose and expression estimation, face recognition, and face detection in cluttered scenes. In addition to these analysis applications, we show how the example-based technique can also be used as a novel method for image synthesis that is for computer graphics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104379. 	[Title] GA-RBF: A Self-Optimising RBF Network [Abstract] The effects of a neural network's topology on its performance are well known, yet the question of finding optimal configurations automatically remains largely open. This paper proposes a solution to this problem for RBF networks. A self- optimising approach, driven by an evolutionary strategy, is taken. The algorithm uses output information and a computationally efficient approximation of RBF networks to optimise the K-means clustering process by co-evolving the two determinant parameters of the network's layout: the number of centroids and the centroids' positions. Empirical results demonstrate promise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13885. 	[Title] Learning Controllers for Industrial Robots [Abstract] One of the most significant cost factors in robotics applications is the design and development of real-time robot control software. Control theory helps when linear controllers have to be developed, but it doesn't sufficiently support the generation of non-linear controllers, although in many cases (such as in compliance control), nonlinear control is essential for achieving high performance. This paper discusses how Machine Learning has been applied to the design of (non-)linear controllers. Several alternative function approximators, including Multilayer Perceptrons (MLP), Radial Basis Function Networks (RBFNs), and Fuzzy Controllers are analyzed and compared, leading to the definition of two major families: Open Field Function Function Approximators and Locally Receptive Field Function Approximators. It is shown that RBFNs and Fuzzy Controllers bear strong similarities, and that both have a symbolic interpretation. This characteristics allows for applying both symbolic and statistic learning algorithms to synthesize the network layout from a set of examples and, possibly, some background knowledge. Three integrated learning algorithms, two of which are original, are described and evaluated on experimental test cases. The first test case is provided by a robot KUKA IR-361 engaged into the "peg-into-hole" task, whereas the second is represented by a classical prediction task on the Mackey-Glass time series. From the experimental comparison, it appears that both Fuzzy Controllers and RBFNs synthesised from examples are excellent approximators, and that, in practice, they can be even more accurate than MLPs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #42847. 	[Title] GROWING RADIAL BASIS FUNCTION NETWORKS [Abstract] This paper presents and evaluates two algorithms for incrementally constructing Radial Basis Function Networks, a class of neural networks which looks more suitable for adtaptive control applications than the more popular backpropagation networks. The first algorithm has been derived by a previous method developed by Fritzke, while the second one has been inspired by the CART algorithm developed by Breiman for generation regression trees. Both algorithms proved to work well on a number of tests and exhibit comparable performances. An evaluation on the standard case study of the Mackey-Glass temporal series is reported. Please summarise all the given information.

Paper #42848 has the title `Evolving Fuzzy Prototypes for Efficient Data Clustering  
` and the abstract `number of prototypes used to represent each class, the position of each prototype within its class and the membership function associated with each prototype. This paper proposes a novel, evolutionary approach to data clustering and classification which overcomes many of the limitations of traditional systems. The approach rests on the optimisation of both the number and positions of fuzzy prototypes using a real-valued genetic algorithm (GA). Because the GA acts on all of the classes at once, the system benefits naturally from global information about possible class interactions. In addition, the concept of a receptive field for each prototype is used to replace the classical distance-based membership function by an infinite fuzzy support, multidimensional, Gaussian function centred over the prototype and with unique variance in each dimension, reflecting the tightness of the cluster. Hence, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. Most data clustering algorithms, including the popular K-means algorithm, require a priori knowledge about the problem domain to fix the number and starting positions of the prototypes. Although such knowledge may be assumed for domains whose dimensionality is fairly small or whose underlying structure is relatively intuitive, it is clearly much less accessible in hyper-dimensional settings, where the number of input parameters may be very large. Classical systems also suffer from the fact that they can only define clusters for one class at a time. Hence, no account is made of potential interactions among classes. These drawbacks are further compounded by the fact that the ensuing classification is typically based on a fixed, distance-based membership function for all prototypes. This paper proposes a novel approach to data clustering and classification which overcomes the aforementioned limitations of traditional systems. The model is based on the genetic evolution of fuzzy prototypes. A real-valued genetic algorithm (GA) is used to optimise both the number and positions of prototypes. Because the GA acts on all of the classes at once and measures fitness as classification accuracy, the system naturally profits from global information about class interaction. The concept of a receptive field for each prototype is also presented and used to replace the classical, fixed distance-based function by an infinite fuzzy support membership function. The new membership function is inspired by that used in the hidden layer of RBF networks. It consists of a multidimensional Gaussian function centred over the prototype and with a unique variance in each dimension that reflects the tightness of the cluster. During classification, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. 
`.
Question: Which category should Paper #42848 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #42848 has the title `Evolving Fuzzy Prototypes for Efficient Data Clustering  
` and the abstract `number of prototypes used to represent each class, the position of each prototype within its class and the membership function associated with each prototype. This paper proposes a novel, evolutionary approach to data clustering and classification which overcomes many of the limitations of traditional systems. The approach rests on the optimisation of both the number and positions of fuzzy prototypes using a real-valued genetic algorithm (GA). Because the GA acts on all of the classes at once, the system benefits naturally from global information about possible class interactions. In addition, the concept of a receptive field for each prototype is used to replace the classical distance-based membership function by an infinite fuzzy support, multidimensional, Gaussian function centred over the prototype and with unique variance in each dimension, reflecting the tightness of the cluster. Hence, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. Most data clustering algorithms, including the popular K-means algorithm, require a priori knowledge about the problem domain to fix the number and starting positions of the prototypes. Although such knowledge may be assumed for domains whose dimensionality is fairly small or whose underlying structure is relatively intuitive, it is clearly much less accessible in hyper-dimensional settings, where the number of input parameters may be very large. Classical systems also suffer from the fact that they can only define clusters for one class at a time. Hence, no account is made of potential interactions among classes. These drawbacks are further compounded by the fact that the ensuing classification is typically based on a fixed, distance-based membership function for all prototypes. This paper proposes a novel approach to data clustering and classification which overcomes the aforementioned limitations of traditional systems. The model is based on the genetic evolution of fuzzy prototypes. A real-valued genetic algorithm (GA) is used to optimise both the number and positions of prototypes. Because the GA acts on all of the classes at once and measures fitness as classification accuracy, the system naturally profits from global information about class interaction. The concept of a receptive field for each prototype is also presented and used to replace the classical, fixed distance-based function by an infinite fuzzy support membership function. The new membership function is inspired by that used in the hidden layer of RBF networks. It consists of a multidimensional Gaussian function centred over the prototype and with a unique variance in each dimension that reflects the tightness of the cluster. During classification, the notion of nearest-neighbour is replaced by that of nearest attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP. 
`.
Question: Which category should Paper #42848 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms