Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Operators for transforming kernels into quasi-local kernels that improve SVM accuracy

Motivated by the crucial role that locality plays in various learning approaches, we present, in the framework of kernel machines for classification, a novel family of operators on kernels able to integrate local information into any kernel obtaining quasi-local kernels. The quasi-local kernels maintain the possibly global properties of the input kernel and they increase the kernel value as the points get closer in the feature space of the input kernel, mixing the effect of the input kernel with a kernel which is local in the feature space of the input one. If applied on a local kernel the operators introduce an additional level of locality equivalent to use a local kernel with non-stationary kernel width. The operators accept two parameters that regulate the width of the exponential influence of points in the locality-dependent component and the balancing between the feature-space local component and the input kernel. We address the choice of these parameters with a data-dependent strategy. Experiments carried out with SVM applying the operators on traditional kernel functions on a total of 43 datasets with di®erent characteristics and application domains, achieve very good results supported by statistical significance

Neighborhood Counting Measure Metric and Minimum Risk Metric: An empirical comparison

Wang in a PAMI paper proposed Neighborhood Counting Measure (NCM) as a similarity measure for the k-nearest neighbors classification algorithm. In his paper, Wang mentioned Minimum Risk Metric (MRM) an earlier method based on the minimization of the risk of misclassification. However, Wang did not compare NCM with MRM because of its allegedly excessive computational load. In this letter, we empirically compare NCM against MRM on k-NN with k=1, 3, 5, 7 and 11 with decision taken with a voting scheme and k=21 with decision taken with a weighted voting scheme on the same datasets used by Wang. Our results shows that MRM outperforms NCM for most of the k values tested. Moreover, we show that the MRM computation is not so probihibitive as indicated by Wang. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Assessment of SVM Reliability for Microarray Data Analysis

The goal of our research is to provide techniques that can assess and validate the results of SVM-based analysis of microarray data. We present preliminary results of the effect of mislabeled training samples. We conducted several systematic experiments on artificial and real medical data using SVMs. We systematically flipped the labels of a fraction of the training data. We show that a relatively small number of mislabeled examples can dramatically decrease the performance as visualized on the ROC graphs. This phenomenon persists even if the dimensionality of the input space is drastically decreased, by using for example feature selection. Moreover we show that for SVM recursive feature elimination, even a small fraction of mislabeled samples can completely change the resulting set of genes. This work is an extended version of the previous paper [MBN04]

The Evaluation of the Communicative Effect

Aim of our research is an analysis of the inferential processes involved in a speaker's evaluation of the communicative effect achieved on a hearer. We present a computational model where such evaluation process relies on two main factors which may vary according to their strength: 1. the verbal commitment of the hearer to play his role in the behavioral game actually bid by the speaker, 2. the personal beliefs of the speaker concerning bearer's beliefs. The hypothesis was tested as follows. First, we devised a questionnaire in order to collect human subjects' evaluations of communicative effects. Subjects were required to consider some scenarios and to identify themselves with a speaker. Their task was to evaluate, for each scenario, the conmunicative effect they had reached on the hearer (acceptance to play the game, refusal, or indecision). Then, we implemented our computational model in a connectionist network; we chose a set of input variables whose combination describes all the scenarios, and we used part of the experimental data to train the network. Finally, we compared the outputs of the network with the evaluations performed by the human subjects. The results are satisfactory

A MultiAgent System for Choosing Software Patterns

Software patterns enable an efficient transfer of design experience by documenting common solutions to recurring design problems. They contain valuable knowledge that can be reused by others, in particular, by less experienced developers. Patterns have been published for system architecture and detailed design, as well as for specific application domains (e.g. agents and security). However, given the steadily growing number of patterns in the literature and online repositories, it can be hard for non-experts to select patterns appropriate to their needs, or even to be aware of the existing patterns. In this paper, we present a multi-agent system that supports developers in choosing patterns that are suitable for a given design problem. The system implements an implicit culture approach for recommending patterns to developers based on the history of decisions made by other developers regarding which patterns to use in related design problems. The recommendations are complemented with the documents from a pattern repository that can be accessed by the agents. The paper includes a set of experimental results obtained using a repository of security patterns. The results prove the viability of the proposed approach

Personal Agents for Implicit Culture Support

We present an implementation of a multi-agent system that aims at solving the problem of tacit knowledge transfer by means of experiences sharing. In particular, we consider experiences of use of pieces of information. Each agent incorporates a system for implicit culture support (SICS) whose goal is to realize the acceptance of the suggested information. The SICS permits a transparent (implicit) sharing of the information about the use, e.g., requesting and accepting pieces of information

A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation

The k-nearest neighbors (k-NN) is a basic machine learning (ML) algorithm, and several quantum versions of it, employing different distance metrics, have been presented in the last few years. Although the Euclidean distance is one of the most widely used distance metrics in ML, it has not received much consideration in the development of these quantum variants. In this article, a novel quantum k-NN algorithm based on the Euclidean distance is introduced. Specifically, the algorithm is characterized by a quantum encoding requiring a low number of qubits and a simple quantum circuit not involving oracles, aspects that favor its realization. In addition to the mathematical formulation and some complexity observations, a detailed empirical evaluation with simulations is presented. In particular, the results have shown the correctness of the formulation, a drop in the performance of the algorithm when the number of measurements is limited, the competitiveness with respect to some classical baseline methods in the ideal case, and the possibility of improving the performance by increasing the number of measurements