Recursive algorithms for pattern classification using misclassified samples

Recursive algorithms for pattern classification of misclassified sample

Maximum likelhood identification of stochastic linear systems

Maximum likelihood identification of stochastic linear system

Sequential coding schemes for an additive noise channel with a noisy feedback link Technical report, May - Aug. 1966

Sequential coding scheme for additive Gaussian channel with noisy feedback lin

Maximum likelihood identification of stochastic linear systems

Maximum likelihood identification of stochastic linear system

On behavior strategy solutions in finite extended decision processes

Techniques for finding best behavior strategies on arbitrary information collection scheme

A new method of recursive estimation in discrete linear systems

Recursive estimation equation giving algorithms for prediction, filtering, and signal smoothing in discrete linear system

Uncertainty Management and Evidential Reasoning with Structured Knowledge

This research addresses two intensive computational problems of reasoning under uncertainty in artificial intelligence. The first problem is to study the strategy for belief propagation over networks. The second problem is to explore properties of operations which construe the behaviour of those factors in the networks. In the study of operations for computing belief combination over a network model, the computational characteristics of operations are modelled by a set of axioms which are in conformity with human inductive and deductive reasoning. According to different topological connection of networks, we investigate four types of operations. These operations successfully present desirable results in the face of dependent, less informative, and conflicting evidences. As the connections in networks are complex, there exists a number of possible ways for belief propagation. An efficient graph decomposition technique has been used which converts the complicated networks into simply connected ones. This strategy integrates the logic and probabilistic aspects inference, and by using the four types of operations for its computation it gains the advantage of better description of results (interval-valued representation) and less information needed. The performance of this proposed techniques can be seen in the example for assessing civil engineering structure damage and results are in tune with intuition of practicing civil engineers

Modeling, Estimation, and Pattern Analysis of Random Texture on 3-D Surfaces

To recover 3-D structure from a shaded and textural surface image involving textures, neither the Shape-from-shading nor the Shape-from-texture analysis is enough, because both radiance and texture information coexist within the scene surface. A new 3-D texture model is developed by considering the scene image as the superposition of a smooth shaded image and a random texture image. To describe the random part, the orthographical projection is adapted to take care of the non-isotropic distribution function of the intensity due to the slant and tilt of a 3-D textures surface, and the Fractional Differencing Periodic (FDP) model is chosen to describe the random texture, because this model is able to simultaneously represent the coarseness and the pattern of the 3-D texture surface, and enough flexible to synthesize both long-term and short-term correlation structures of random texture. Since the object is described by the model involving several free parameters and the values of these parameters are determined directly from its projected image, it is possible to extract 3-D information and texture pattern directly from the image without any preprocessing. Thus, the cumulative error obtained from each pre-processing can be minimized. For estimating the parameters, a hybrid method which uses both the least square and the maximum likelihood estimates is applied and the estimation of parameters and the synthesis are done in frequency domain. Among the texture pattern features which can be obtained from a single surface image, Fractal scaling parameter plays a major role for classifying and/or segmenting the different texture patterns tilted and slanted due to the 3-dimensional rotation, because of its rotational and scaling invariant properties. Also, since the Fractal scaling factor represents the coarseness of the surface, each texture pattern has its own Fractal scale value, and particularly at the boundary between the different textures, it has relatively higher value to the one within a same texture. Based on these facts, a new classification method and a segmentation scheme for the 3-D rotated texture patterns are develope

UNSUPERVISED CLASSIFICATION AND CHOICE OF CLASSES: BAYESIAN APPROACH

We have given a solution to the problem of unsupervised classifica,tioll of multidinlensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior -probability density given the data. The concept of partition as a variable to be estimated hasn\u27t been considered. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information &about a class while assigning its prohability density. It can also ut,ilize any available training samples that arise from different classes. We provide a. descent algorithm that starts with an arbitrary partition of the data, and iteratively computes the MAP estimates. We also focus on robust regression which is a special case of unsupervised classification with two classes; inliers and outliers. The problem of intensity image segmentation is posed as an unsupervised classification problem and solved using the Bayesian formulation a multiscale set up. The proposed method is also applied to data sets that occur in statistical literature and target tracking. The results ohbtained demonstrate the power of Bayesian approach for unsupervised classification