Search results for "Discriminative model"
showing 10 items of 44 documents
Image Recognition through Incremental Discriminative Common Vectors
2010
An incremental approach to the discriminative common vector (DCV) method for image recognition is presented. Two different but equivalent ways of computing both common vectors and corresponding subspace projections have been considered in the particular context in which new training data becomes available and learned subspaces may need continuous updating. The two algorithms are based on either scatter matrix eigendecomposition or difference subspace orthonormalization as with the original DCV method. The proposed incremental methods keep the same good properties than the original one but with a dramatic decrease in computational burden when used in this kind of dynamic scenario. Extensive …
Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines
2020
Tsetlin Machines (TMs) are an interpretable pattern recognition approach that captures patterns with high discriminative power from data. Patterns are represented as conjunctive clauses in propositional logic, produced using bandit-learning in the form of Tsetlin Automata. In this work, we propose a TM-based approach to two common Natural Language Processing (NLP) tasks, viz. Sentiment Analysis and Semantic Relation Categorization. By performing frequent itemset mining on the patterns produced, we show that they follow existing expert-verified rule-sets or lexicons. Further, our comparison with other widely used machine learning techniques indicates that the TM approach helps maintain inter…
A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data …
2013
Abstract Background Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results A procedure is proposed for the assessment of the discriminative ability of a distance functi…
Arabic Named Entity Recognition: A Feature-Driven Study
2009
The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact …
A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions
2021
International audience; When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), gene…
Personality traits and parenting: neuroticism, extraversion, and openness to experience as discriminative factors
2003
This study used variable‐ and person‐oriented approaches to examine the relationship between personality traits (at age 33) and parenting (at age 36) among 94 mothers and 78 fathers. The SEM revealed that Openness to Experience (O), low Neuroticism (N), and Extraversion (E) were related to parental nurturance; low O to parental restrictiveness; and low N to parental knowledge about the child's activities. Cluster analysis based on the three parenting factors yielded six gender‐related parenting types with distinguishable personality profiles. Authoritative parents (mostly mothers) and emotionally involved parents (mostly fathers), who were high in nurturance and high to moderate in parenta…
The role of perspective on students’ use of multiple documents to solve an openended Task
2013
AbstractMcCrudden & Schraw (2007) show how students’ interaction with a text is mediated by the instructions they are presented with. Specifically, perspective instructions prompt readers to use background knowledge to evaluate text from an assigned point of view. Research on perspective in single texts has demonstrated that readers confer relevance on text segments that are consistent with the assigned perspective (Pichert & Anderson, 1977). This work has tested the effects of perspective when reading conflicting information from multiple documents to perform an open-ended task, as well as has studied if the trustworthiness of a source exerts any influence on students’ decision to use info…
Keypoint descriptor matching with context-based orientation estimation
2014
Abstract This paper presents a matching strategy to improve the discriminative power of histogram-based keypoint descriptors by constraining the range of allowable dominant orientations according to the context of the scene under observation. This can be done when the descriptor uses a circular grid and quantized orientation steps, by computing or providing a global reference orientation based on the feature matches. The proposed matching strategy is compared with the standard approaches used with the SIFT and GLOH descriptors and the recent rotation invariant MROGH and LIOP descriptors. A new evaluation protocol based on an approximated overlap error is presented to provide an effective an…
Texture analysis for infarcted myocardium detection on delayed enhancement MRI
2017
Detection of infarcted myocardium in the left ventricle is achieved with delayed enhancement magnetic resonance imaging (DE-MRI). However, manual segmentation is tedious and prone to variability. We studied three texture analysis methods (run-length matrix, co-occurrence matrix, and autoregressive model) in combination with histogram features to characterize the infarcted myocardium. We evaluated 10 patients with chronic infarction to select the most discriminative features and to train a support vector machine (SVM) classifier. The classifier model was then used to segment five human hearts from the STACOM DE-MRI challenge at MICCAI 2012. The Dice coefficient was used to compare the segmen…
An overview of incremental feature extraction methods based on linear subspaces
2018
Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alter…