Search results for "Intelligence"
showing 10 items of 6959 documents
Overlapped moving windows followed by principal component analysis to extract information from chromatograms and application to classification analys…
2015
Variable generation from chromatograms is conveniently accomplished using unsupervised rather than manual techniques. With unsupervised techniques, there is no need for selecting a few peaks for manual integration and valuable information is quickly and efficiently collected. The generation of variables can be performed by using either peak searching or moving window (MW) strategies. With a MW approach, the peaks are ignored and many variables, only part of them carrying information, are generated. Thus, variable generation by MWs should be followed by data compression to generate the variables to be further used for classification or quantitation purposes. In this work, unsupervised proces…
Incremental linear model trees on massive datasets
2013
The existence of massive datasets raises the need for algorithms that make efficient use of resources like memory and computation time. Besides well-known approaches such as sampling, online algorithms are being recognized as good alternatives, as they often process datasets faster using much less memory. The important class of algorithms learning linear model trees online (incremental linear model trees or ILMTs in the following) offers interesting options for regression tasks in this sense. However, surprisingly little is known about their performance, as there exists no large-scale evaluation on massive stationary datasets under equal conditions. Therefore, this paper shows their applica…
DenseYOLO: Yet Faster, Lighter and More Accurate YOLO
2020
As much as an object detector should be accurate, it should be light and fast as well. However, current object detectors tend to be either inaccurate when lightweight or very slow and heavy when accurate. Accordingly, determining tolerable tradeoff between speed and accuracy of an object detector is not a simple task. One of the object detectors that have commendable balance of speed and accuracy is YOLOv2. YOLOv2 performs detection by dividing an input image into grids and training each grid cell to predict certain number of objects. In this paper we propose a new approach to even make YOLOv2 more fast and accurate. We re-purpose YOLOv2 into a dense object detector by using fine-grained gr…
Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
2012
Clustering is one of the most well known activities in scien- tific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identifi- cation of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained promi- nence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of predic- tion, but the slowest in terms of time. Unfortunately…
Hands-on or Video-based Learning with ANTicipation? A Comparative Approach to Identifying Student Motivation and Learning Enjoyment During a Lesson a…
2015
The observation of living animals in school laboratories provides authentic views of biological research. Various studies stress the importance of primary experiences in biology classes. However, educational films may serve as an alternative in some cases. The aim of this study was to investigate student motivation before and after treatments, including (1) an educational film, (2) a hands-on activity with living animals accompanied by an educational film and (3) a hands-on activity with living animals. We investigated the influence of teaching method, gender and class level on student motivation and learning enjoyment. In all treatments, Temnothorax ants were addressed, which can be easily…
The PASSI and Agile PASSI MAS Meta-models Compared with a Unifying Proposal
2005
A great number of processes for multi-agent systems design have been presented in last years to support the different approaches to agent-oriented design; each process is specific for a particular class of problems and it instantiates a specific MAS meta-model. These differences produce inconsistences and overlaps: a MAS meta-model may define a term not referred by another, or the same term can be used with a different meaning. We think that the lack of a standardization may cause a significant delay to the diffusion of the agent paradigm outside research context. Working for this unification goal, it is also necessary to define in unambiguous way the terms of the agent model and their rela…
Methods of Digital Hilbert Optics in the Analysis and Objects’ Recognition
2016
This paper describes methods on how to increase the effectiveness of objects’ pictures identification based on correlation methods. The main concept of increasing the discriminant effectiveness is based on highlighting of characteristic points of recognized objects by applying Hilbert transformations. Study of the effectiveness of Digital Hilber Optics (DHO) have been performed on a set of aircrafts, whose models rendered first as binary images, and then as grayscale. It has been performed a very detailed analysis of requirements on resources of information system’s which would in a real world support the discriminatory decision of objects’ class for which the sample database has been creat…
One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices
2012
In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the…
On Duality in Learning and the Selection of Learning Teams
1996
AbstractPrevious work in inductive inference dealt mostly with finding one or several machines (IIMs) that successfully learn collections of functions. Herein we start with a class of functions and considerthe learner setof all IIMs that are successful at learning the given class. Applying this perspective to the case of team inference leads to the notion ofdiversificationfor a class of functions. This enable us to distinguish between several flavours of IIMs all of which must be represented in a team learning the given class.
Evaluative linguistic expressions vs. fuzzy categories
2015
In this paper, we discuss the distinction between categories characterized by verbal labels taken from a fuzzy rating scale and special class of linguistic expressions, called evaluative. The latter form a general class of expressions that includes gradable and evaluative adjectives and their hedges. First, we will provide a brief linguistic analysis of them. Then we outline basic principles for construction of the mathematical model of semantics of evaluative expressions. In Section 3 we will analyze the concepts of rating scale with verbal labels (fuzzy rating scale), their semantics and demonstrate that the latter cannot be identified with the semantics of evaluative expressions. Finally…