Linear discriminant analysis (LDA) is a versatile method in all pattern recognition fields but it suffers from some limitations. In a multi-class problem, when samples of a class are far from other classes samples, it leads to bias of the whole decision boundaries of LDA in favor of the farthest class. To overcome this drawback, this study is aimed at minimizing this bias by redefining the between- and within-class scatter matrices via incorporating weight vectors derived from Fisher value of classes pairs. After projecting the input patterns into a lower-dimensional space in which the class samples are more separable, a new version of nearest neighbor (NN) method with an adaptive distance measure is employed to classify the transformed samples. To speed up the adaptive distance routine, an iterative learning algorithm that minimizes the error rate is presented. This efficient method is applied to six standard datasets driven from the UCI repository dataset and test results are evaluated from three aspects in terms of accuracy, robustness, and complexity. Results show the supremacy of the proposed two-layer classifier in comparison with the combination of different versions of LDA and NN methods from the three points of view. Moreover, the proposed classifier is assessed in the noisy environment of those datasets and the achieved results confirm the high robustness of the introduced scheme when compared to others.
Problems with parasitic infections and their interspecies transmissions are common in zoological gardens and could pose serious health damage to captive animals. This study presents results of eight-year monitoring of intestinal parasites in animals from Zoo Ljubljana, Slovenia. A total of 741 faecal samples from 40 animal species were collected two to four times per year and examined microscopically. Intestinal parasites were detected in 45% of samples, with detection of helminths (Cestoda, Nematoda - Ascaridida, Enoplida, Strongylida, Oxyurida, Rhabditida and Trichurida) and protists (Apicomplexa and Ciliophora) in 25% and 13% of samples, respectively; mixed infection was found in 7% of samples. The mostly infected were ungulates (61%), followed by reptiles (44%), ratites (29%), primates (22%) and carnivores (7%). During the observation period, the number of infected animal species increased from 8 to 25. This is the first long-term monitoring study of intestinal parasites in zoo animals from Slovenia. Routine monitoring of parasitic infection and regular deworming and hygienic measures are necessary to prevent gastrointestinal infections in captive animals., Pavel Kvapil, Marjan Kastelic, Alenka Dovč, Eva Bártová, Petr Čížek, Natacha Lima, Špela Štrus., and Obsahuje bibliografii
An Electronic Performance Support System (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project (JUst-in-tiMe Performance support systém for dynamic organizations, co-funded by POR Puglia 2000-2006 - Mis. 3.13, Sostegno agli Investimenti in Ricerca Industriale, Sviluppo Precompetitivo e Trasferimento Tecnologico) aims at integrating an EPSS with a hybrid recommender system.
Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. The main contribution of this paper is a content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles in which user preferences are stored, instead of comparing their rating styles. A distinctive feature of our systém is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in WordNet. This model, named ``semantic user profile'', is exploited by the hybrid recommender in the neighborhood formation process.
t is commonly known that absolute gauge integrability, or Henstock-Kurzweil (H-K) integrability implies Lebesgue integrability. In this article, we are going to present another proof of that fact which utilizes the basic definitions and properties of the Lebesgue and H-K integrals.
About 30-50% of the world human population are infected with the protozoan parasite Toxoplasma gondii (Nicolle et Manceaux, 1908). Latent toxoplasmosis has many specific behavioural and physiological effects on the human body and influences the course of pregnancy, including secondary sex ratio of children of infected mothers. It was suggested that an increased concentration of glucose could be the proximate cause of increased sex ratio. There are some indirect indications of possible association between toxoplasmosis and certain forms of diabetes. Here we searched for a possible link between latent toxoplasmosis and the level of glucose in the blood. In a cross-sectional study, we found that pregnant women with latent toxoplasmosis had significantly higher blood glucose levels during the oral glucose tolerance test (n = 191, p = 0.010; the level of fasting plasma glucose: mean = 5.04 mmol/l vs mean = 4.88 mmol/l; blood glucose level at 1 hour mean = 7.73 mmol/l vs mean = 6.89 mmol/l and blood glucose level at two hours mean = 6.43 mmol/l vs mean = 5.74 mmol/l) and higher prevalence (19.5 %) of gestational diabetes mellitus (n = 532, p = 0.033, odds ratio = 1.78) in the 24-28th gestational weeks than T. gondii-free women (12.0 %). Increased level of glucose and increased incidence of gestational diabetes mellitus could have considerable clinical impact as contributors to the development of the metabolic syndrome and type 2 diabetes in T. gondii-infected women. Our results also brought the first empirical support for the hypothesis that the glucose concentration may play a role in T. gondii-associated offspring sex ratio shifts., Šárka Kaňková, Jaroslav Flegr, Pavel Calda., and Obsahuje bibliografii
The main purpose of this paper is to prove that the elliptic curve $E\colon y^2=x^3+27x-62$ has only the integral points $(x, y)=(2, 0)$ and $(28844402, \pm 154914585540)$, using elementary number theory methods and some known results on quadratic and quartic Diophantine equations.
We consider the weighted space $W_1^{(2)}(\mathbb R,q)$ of Sobolev type $$ W_1^{(2)}(\mathbb R,q)=\left \{y\in A_{\rm loc}^{(1)}(\mathbb R)\colon \|y''\|_{L_1(\mathbb R)}+\|qy\|_{L_1(\mathbb R)}<\infty \right \} $$ and the equation $$ - y''(x)+q(x)y(x)=f(x),\quad x\in \mathbb R. \leqno (1) $$ Here $f\in L_1(\mathbb R)$ and $0\le q\in L_1^{\rm loc}(\mathbb R).$ \endgraf We prove the following: \item {1)} The problems of embedding $W_1^{(2)}(\mathbb R,q)\hookrightarrow L_1(\mathbb R)$ and of correct solvability of (1) in $L_1(\mathbb R) $ are equivalent; \item {2)} an embedding $W_1^{(2)}(\mathbb R,q)\hookrightarrow L_1(\mathbb R) $ exists if and only if $$\exists a>0\colon \inf _{x\in \mathbb R}\int _{x-a}^{x+a} q(t) {\rm d} t>0.$$.
Recently, the the support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first step is feature extraction. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. The PCA linearly transforms the original inputs into new uncorrelated features. The KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the sunspot data, Santa Fe data set A and five real futures contracts, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, among the three methods, there is the best performance in the KPCA feature extraction, followed by the ICA feature extraction.
Recently, a support vector machine (SVM) has been receiving increasing attention in the field of regression estimation due to its remarkable characteristics such as good generalization performance, the absence of local minima and sparse representation of the solution. However, within the SVMs framework, there are very few established approaches for identifying important features. Selecting significant features from all candidate features is the first step in regression estimation, and this procedure can improve the network performance, reduce the network complexity, and speed up the training of the network.
This paper investigates the use of saliency analysis (SA) and genetic algorithm (GA) in SVMs for selecting important features in the context of regression estimation. The SA measures the importance of features by evaluating the sensitivity of the network output with respect to the feature input. The derivation of the sensitivity of the network output to the feature input in terms of the partial derivative in SVMs is presented, and a systematic approach to remove irrelevant features based on the sensitivity is developed. GA is an efficient search method based on the mechanics of natural selection and population genetics. A simple GA is used where all features are mapped into binary chromosomes with a bit "1" representing the inclusion of the feature and a bit of "0" representing the absence of the feature. The performances of SA and GA are tested using two simulated non-linear time series and five real financial time series. The experiments show that with the simulated data, GA and SA detect the same true feature set from the redundant feature set, and the method of SA is also insensitive to the kernel function selection. With the real financial data, GA and SA select different subsets of the features. Both selected feature sets achieve higher generation performance in SVMs than that of the full feature set. In addition, the generation performance between the selected feature sets of GA and SA is similar. All the results demonstrate that that both SA and GA are effective in the SVMs for identifying important features.
This paper proposes an endogenous human resources selection process by using linguistic information from a competency management perspective. We consider different sets of appraisers taking part in the evaluation process, having a different knowledge about the candidates that are being evaluated. Then, appraisers can express their assessments in different linguistic domains according to their knowledge. The proposed method converts each linguistic label into a fuzzy set on a common domain. Candidates are ranked by using different aggregation operators in order to allow the management team to make a final decision.