Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Self-Organising and Competitive Learning Neural Networks <s> A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture self-organizes and self-stabilizes its recognition codes in response to arbitrary orderings of arbitrarily many and arbitrarily complex binary input patterns. Top-down attentional and matching mechanisms are critical in self-stabilizing the code learning process. The architecture embodies a parallel search scheme which updates itself adaptively as the learning process unfolds. After learning self-stabilizes, the search process is automatically disengaged. Thereafter input patterns directly access their recognition codes without any search. Thus recognition time does not grow as a function of code complexity. A novel input pattern can directly access a category if it shares invariant properties with the set of familiar exemplars of that category. These invariant properties emerge in the form of learned critical feature patterns, or prototypes. The architecture possesses a context-sensitive self-scaling property which enables its emergent critical feature patterns to form. They detect and remember statistically predictive configurations of featural elements which are derived from the set of all input patterns that are ever experienced. Four types of attentional process—priming, gain control, vigilance, and intermodal competition—are mechanistically characterized. Top—down priming and gain control are needed for code matching and self-stabilization. Attentional vigilance determines how fine the learned categories will be. If vigilance increases due to an environmental disconfirmation, then the system automatically searches for and learns finer recognition categories. A new nonlinear matching law (the ⅔ Rule) and new nonlinear associative laws (the Weber Law Rule, the Associative Decay Rule, and the Template Learning Rule) are needed to achieve these properties. All the rules describe emergent properties of parallel network interactions. The architecture circumvents the noise, saturation, capacity, orthogonality, and linear predictability constraints that limit the codes which can be stably learned by alternative recognition models. <s> BIB001 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Self-Organising and Competitive Learning Neural Networks <s> Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets. <s> BIB002 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Self-Organising and Competitive Learning Neural Networks <s> The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed. > <s> BIB003
As far as we know, networks which use an unsupervised learning approach have not been employed for CBR system design (see Fig. 2 for reference) . In fact, self-organising and competitive learning neural networks have been widely used in pattern feature extraction and classification. Such networks can create a meaningful coordinate system for different input features through an unsupervised learning paradigm and a competitive process among the network nodes BIB002  . Two representative examples of this type of network paradigm are the SelfOrganising Map (SOM) network BIB003 and the Adaptive Resonance Theory (ART) network BIB001 . The SOM consists of an input layer and an output layer, with feedforward connections from input to output and lateral connections between nodes in the output layer. Usually, nodes in the input and output layers are arranged in a two-dimensional array, respectively. For a given input pattern, the output nodes compete among themselves to be activated or fired, with the result that only one output node is on. Through a competitive learning course, the SOM can capture the underlying structure of the input patterns, and perform a topology preserving map in which the spatial location of the activated nodes in the output array correspond to intrinsic topographic features of the input patterns. Using the SOM in CBR system design, the high dimensional case feature vectors can be visualised (i.e. projected linearly) in a two-dimensional space, which is very helpful for understanding the underlying structure of the cases and analysing the relationship among cases. Further, based on some pattern matching criteria, for example, the k-nearest neighbour algorithm, case retrieval can be easily performed in the low dimensional space. The ART network is specified by a two-layer network, where the input and output layers are labelled as comparison and recognition layers, respectively. There are two sets of connections, bottom-up and top-down connections, between the nodes in the two layers. A bottom-up connection associated with a recognition node respects a stored pattern. A binary version of the same pattern is also stored in a corresponding connection in the comparison layer, which is used to control the network training process, when necessary. In addition, three functional modules are used to provide control functions needed for the network training and classification phases. When a new input vector is applied, only the output node with a weight vector best matching the input pattern is activated, and all other nodes are inhibited. Once this occurs, both the bottom-up and top-down connecting weights associated with the fired node are modified to make the stored patterns more like the input pattern. If no stored pattern matches the input pattern, within a predefined vigilance parameter, a new pattern category is created by adding a new node in the recognition layer and setting its associated weights to match the input pattern. In this way, the ART resolves the stability-plasticity dilemma for a learning process: adding new additional pattern classes whilst keeping the stored patterns obtained through previous learning. Obviously, such a property is important for a CBR system. Therefore, applying ART in CBR systems is helpful in developing an effective strategy for case retrieval and case updating. 