We have divided this area into categories and cross-indexed the references accordingly.

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

The best examples of this type are probably capable of being extended to process new classes of patterns (Grimsdale et al.,

Except for their inability to recognize patterns, machines (or, more accurately, the programs that tell machines what to do) have now met most of the classic criteria of intelligence that skeptics have proposed. They vised can outperform their designers: The checker-playing program de tion by Arthur L. Samuel of International Business Machines Corpora (1959a) usually beats him. They are original: The "Logic Theorist," a creation of a group from the Carnegie Institute of Technology and the RAND Corporation [Newell, Simon, and Shaw (1956

When working from of a small set of primitives and the statement of a such representations, lexical choice is often a nonissue program's knowledge as a set of expressions over these since each term can be uniquely associated with a natural primitives plus a set of constant terms for individuals.

Segmentation needs to be done Aloimonos (1988), a theory is developed for determining somewhere along the way. If one is working with the twoview the motion of an observer given the flow field over a full case described in Solution Using Point Correspondences 360 degree image sphere. The method is based on the fact (above) and if the motions of the rigid bodies are that the foci of expansion and contraction for an observer small from t1 to t2, the following approach can be tried.

Given a data sample with its feature vector, SC tries to learn a codebook with some codeworks, and approximate the data sample as the linear combination of the codewords. SC assume that only a few codewords in the codebook are enough to represent the data sample, thus the combination coefficients should be sparse, i.e. most of the coefficients are zeros, leaving only a few of them non-zeros. The linear combination coefficients of the data sample could be its new representation. Because they are sparse, the coefficient vector is often referred to as the sparse code. To solve the sparse code, one usually minimizes the approximation error with regard to the codebook and the sparse code, and at the same time seeks the sparsity of the sparse code. Although SC has been used in many pattern recognition applications, such as palmprint recognition [24], dynamic texture recognition [25], human action recognition [26], [27], [28], speech recognition [29], digit recognition [30], image annotation [31], [32], [33], and face recognition [34], in most cases, SC is used as an unsupervised learning method. When SC is performed to the training data set, it is assumed that the class labels of the training samples are unavailable. Then after the sparse codes are learned, they will be used to learn a classifier. Thus the class labels are ignored during the sparse coding procedure.

--This article proposes the use of V ector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The adoption of a V ector Symbolic representation ensures a one-layered design for the approach, while maintaining the previously reported properties and performance characteristics of Hierarchical Graph Neuron, and also improving the noise resistance of the architecture. The proposed architecture enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern. RAPH Neuron (GN) is an approach for memorizing patterns of generic sensor stimuli for later template matching. It is based on the hypothesis that a better associative memory resource can be created by changing the emphasis from high speed sequential CPU processing to parallel network-centric processing [2], [3]. In contrast to contemporary machine learning approaches, GN allows introduction of new patterns in the learning set without the need for retraining. Whilst doing so, it exhibits a high level of scalability i.e. its performance and accuracy do not degrade as the number of stored patterns increases over time. V ector Symbolic Architectures (VSA) [4] are a bio-inspired method of representing concepts and their meaning for modeling cognitive reasoning. It exhibits a set of unique properties which make it suitable for implementation of artificial general intelligence [5], [6], [7], and so, creation of complex systems for sensing and pattern recognition without reliance on complex computation. In the biological world, extremely successful applications of these approaches can be found.

The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed non-target, and therefore they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation based approach; we embed the input data into the dissimilarity space by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented through a weighted Euclidean graphs, which we use to (i) determine the entropy of the data distribution in the dissimilarity space, and at the same time (ii) derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking datasets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique.

This article is an overview of the "SP theory of intelligence". The theory aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived as a brain-like system that receives 'New' information and stores some or all of it in compressed form as 'Old' information. It is realised in the form of a computer model -- a first version of the SP machine. The concept of "multiple alignment" is a powerful central idea. Using heuristic techniques, the system builds multiple alignments that are 'good' in terms of information compression. For each multiple alignment, probabilities may be calculated. These provide the basis for calculating the probabilities of inferences. The system learns new structures from partial matches between patterns. Using heuristic techniques, the system searches for sets of structures that are 'good' in terms of information compression. These are normally ones that people judge to be 'natural', in accordance with the 'DONSVIC' principle -- the discovery of natural structures via information compression. The SP theory may be applied in several areas including 'computing', aspects of mathematics and logic, representation of knowledge, natural language processing, pattern recognition, several kinds of reasoning, information storage and retrieval, planning and problem solving, information compression, neuroscience, and human perception and cognition. Examples include the parsing and production of language including discontinuous dependencies in syntax, pattern recognition at multiple levels of abstraction and its integration with part-whole relations, nonmonotonic reasoning and reasoning with default values, reasoning in Bayesian networks including 'explaining away', causal diagnosis, and the solving of a geometric analogy problem.