15381679
A Modular Neural Network Architecture for Pattern Classification Based on Different Feature Sets
We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g., combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.
34883067
Personalized News Reading via Hybrid Learning
In this paper, we present a personalized news reading prototype where latest news articles published by various on-line news providers are automatically collected, categorized and ranked in light of a user’s habits or interests. Moreover, our system can adapt itself towards a better performance. In order to develop such an adaptive system, we proposed a hybrid learning strategy; supervised learning is used to create an initial system configuration based on user’s feedbacks during registration, while an unsupervised learning scheme gradually updates the configuration by tracing the user’s behaviors as the system is being used. Simulation results demonstrate satisfactory performance.
64402095
Trends in Neural Computation (Studies in Computational Intelligence)
206764065
Adaptive smoothing via contextual and local discontinuities
A novel adaptive smoothing approach is proposed for noise removal and feature preservation where two distinct measures are simultaneously adopted to detect discontinuities in an image. Inhomogeneity underlying an image is employed as a multiscale measure to detect contextual discontinuities for feature preservation and control of the smoothing speed, while local spatial gradient is used for detection of variable local discontinuities during smoothing. Unlike previous adaptive smoothing approaches, two discontinuity measures are combined in our algorithm for synergy in preserving nontrivial features, which leads to a constrained anisotropic diffusion process that inhomogeneity offers intrinsic constraints for selective smoothing. Thanks to the use of intrinsic constraints, our smoothing scheme is insensitive to termination times and the resultant images in a wide range of iterations are applicable to achieve nearly identical results for various early vision tasks. Our algorithm is formally analyzed and related to anisotropic diffusion. Comparative results indicate that our algorithm yields favorable smoothing results, and its application in extraction of hydrographic objects demonstrates its usefulness as a tool for early vision.
41845464
Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I
5764451
Regularized Boost for Semi-Supervised Learning
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes local smoothness constraints among data into account during ensemble learning. In this paper, we introduce a local smoothness regularizer to semi-supervised boosting algorithms based on the universal optimization framework of margin cost functionals. Our regularizer is applicable to existing semi-supervised boosting algorithms to improve their generalization and speed up their training. Comparative results on synthetic, benchmark and real world tasks demonstrate the effectiveness of our local smoothness regularizer. We discuss relevant issues and relate our regularizer to previous work.
15411856
Prediction of the Number of Helices for the Twilight Zone Proteins
Protein structure prediction is one of the core research areas in bioinformatics. This paper addresses the protein secondary structure prediction problem for the twilight zone proteins, which are characterized by low, about 25% homology to the sets of known sequences. The commonly used sequence alignment based algorithms fail to provide accurate prediction for sequences of such low homology, and thus alternative solutions should be sought. We propose a novel method that aims at the prediction of the number of helical structures based on the twilight zone protein sequences. The method is based on a custom designed and compact feature based sequences representation and applies a decision tree prediction algorithm. The performed experimental study shows superiority of the proposed method over three other prediction algorithms and the results provided by YASPIN algorithm, which is a state-of-the-art alignment based secondary structure prediction method designed using low homology sequences
12408273
Multilevel algorithm for a Poisson noise removal model with total-variation regularization
Many commonly used models for the fundamental image processing task of noise removal can deal with Gaussian white noise. However, such Gaussian models are not effective in restoring images with Poisson noise, which is ubiquitous in certain applications. Recently, Le–Chartrand–Asaki derived a new data-fitting term in the variational model for Poisson noise. This paper proposes a multilevel algorithm for efficiently solving this variational model. As expected of a multilevel method, it delivers the same numerical solution many orders of magnitude faster than the standard single-level method of coordinate descent time-marching. Supporting numerical experiments on 2D gray scale images are presented.
15447660
A logical replication-based intrusion detection approach for CSCW system
With the rapid development of CSCW techniques, CSCW system security has been of much attention. Its major challenge comes from malicious attacks, which cannot be handled by traditional security mechanisms, such as authorization, access control, encryption, etc. Although there existed some intrusion detection systems, current researches on intrusion detection are still insufficient in accuracy and efficiency for two reasons: firstly, most of them implement malicious transaction detection only by matching samples with predefined patterns; secondly, most current researches mainly resort to semantic analyzing or even only manual interventions to judge the malicious attacks. Inspired by the replication technique, we propose and implement a novel logical replication-based intrusion detection solution for CSCW system in this paper. It provides an additional layer of defense against application attacks, especially program attacks. The approach entitles the system to detect malicious intrusions efficiently at low false positive rate.
17434449
On Two Multigrid Algorithms for Modeling Variational Multiphase Image Segmentation
In this paper, we present two related multigrid algorithms for multiphase image segmentation. Algorithm I solves the model by Vese-Chan. We first generalize our recently developed multigrid method to this multiphase segmentation model (MG1); we also give a local Fourier analysis for the local smoother which leads to a new and more effective smoother. Although MG1 is found many magnitudes faster than the fast method of additive operator splitting (AOS), both algorithms are not robust with regard to the initial guess. To overcome this dependence on the initial guess, we consider a hierarchical segmentation model which achieves multiphase segmentation by repeated use of the Chan-Vese two-phase model; our algorithm II solves this model by a multigrid algorithm (MG2). Numerical experiments show that both algorithms are efficient and in particular MG2 is more robust than MG1 with respect to initial guesses. AMS subject classifications: 68U10, 65F10, 65K10.
14977777
Zhang neural network without using time-derivative information for constant and time-varying matrix inversion
To obtain the inverses of time-varying matrices in real time, a special kind of recurrent neural networks has recently been proposed by Zhang et al. It is proved that such a Zhang neural network (ZNN) could globally exponentially converge to the exact inverse of a given time-varying matrix. To find out the effect of time-derivative term on global convergence as well as for easier hardware-implementation purposes, the ZNN model without exploiting time-derivative information is investigated in this paper for inverting online matrices. Theoretical results of both constant matrix inversion case and time-varying matrix inversion case are presented for comparative and illustrative purposes. In order to substantiate the presented theoretical results, computer-simulation results are shown, which demonstrate the importance of time derivative term of given matrices on the exact convergence of ZNN model to time-varying matrix inverses.