Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175

USING LATENT SEMANTIC INDEXING FOR DOCUMENT CLUSTERING

Documents with various contents are easily obtained from URLs which are associated with their titles. However, the titles of documents may not describe their contents and they just attract the readers to buy and read them. Therefore, the document clustering based on the same category is important to help users to retrieve information they need. Document clustering is an implementation of data mining task. By using similarity measurement of documents‟ characteristic, they can be clustered based on the same category or topic. High dimensionality of the document representation is due to representing of all substantial words in the vector space model. It is one of problems in document clustering that decreases the cluster quality performance including f-measure, entropy and accuracy. In categorical domain, many research have been conducted to reduce the dimension size of term-document matrix representation until by using keyword base. However, the result is obtained low accuracy in various class sizes of document collections. Therefore, this research is intended to improve the quality and accuracy of document clustering by using a method in information retrieval. A method in information retrieval, Latent Semantic Indexing (LSI), is proposed to reduce the dimension of term-document matrix for document representation. In this work, the LSI method is used to produce the patterns of terms, so that documents can be mapped into concept space. Based on the new representation, the documents are then subjected to the clustering algorithm itself, which is Fuzzy c-Means algorithm. A variant of distance measurement, cosine similarity, is also embedded to this algorithm. The results are then compared with some existing algorithms, which are used for benchmark purposes. The results show that the proposed method obtains high quality cluster and it is superior to the other fuzzy clustering algorithms for category i.e. FCCM, FSKWIC, and Fuzzy CoDoK with accuracy rate of over 90%

Tag Clouds: How format and categorical structure affect categorization judgment

This paper examines how category judgments are influenced by categorical structure and the formatting of tag clouds. Despite the enormous research on categorization, little research has been directed at investigating whether one person can recognize another's categorical structure. A novel approach to measure similarity and categorical structure is proposed. This approach involves the use of latent semantic analyses to compute semantic distances between category exemplars. The empirical domain will be tag clouds, a new development in social computing that provides a particularly useful paradigm for investigating how people identify the categorical structures of others. Three experiments examine how categorical structure and different formatting styles used in tag clouds might affect categorization. Findings reveal that categorization judgments are influenced by categorical structure and tighter structures result in higher accuracy. Format variables such as font size and sorting order were also found to influence accuracy. Future experimental directions are detailed

Court Judgment Decision Support System Based on Medical Text Mining

Medical damage is a common problem faced by hospitals around the world and is widely watched by countries and the World Health Organization. As the number of medical damage dispute lawsuit cases rapidly grows, many countries in the world face the problem how to improve the efficiency of the judicial system under the premise of guaranteeing the quality of the trial. Therefore, in addition to reforming the system, the decision support system will effectively improve judicial decisions. This paper takes medical damage judgment documents in China as example, and proposes a court judgment decision support system (CJ-DSS) based on medical text mining and the automatic classification technology. The system can predict the trail results of the new lawsuit documents according to the previous cases verdict - rejected and non-rejected. Combined with the cases, the study in this paper found that combined feature extraction method does improve the performance of three kinds of classifiers - Support Value Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN), the degree of improved performance is different from using DF-CHI combined feature extraction method. In addition, integrated learning algorithm also improves the classification performance of the overall system

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409