Search results for "Data type"
showing 10 items of 1183 documents
Identification and validation of quasispecies models for biological systems
2009
An identification procedure for biological systems cast as quasi-species models is proposed. Their identification is a challenging problem because of the bilinear dependence on the parameters and their physical constraints. The proposed solution is within the framework of set-membership identification. %The bilinear dependence on parameters of the model and their physical constraints make the present issue challenging. We determine an estimate of the model parameters together with their interval of variability (Uncertainty Intervals), taking into account all the physical constraints. Invalidation/validation is performed on the basis of the predictive capability of the estimated models. The …
Model Identification of a Network as Compressing Sensing
2013
In many applications, it is important to derive information about the topology and the internal connections of dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology, and unveiling an unknown structure as the estimate of a "sparse Wiener filter". A geometric interpretation of the problem in a pre-Hilbert space for wide-sense stochastic processes is provided. We cast the problem as the optimization of a cost function where a set of parameters are used t…
Benchmarking Wilms’ tumor in multisequence MRI data: why does current clinical practice fail? Which popular segmentation algorithms perform well?
2019
Wilms' tumor is one of the most frequent malignant solid tumors in childhood. Accurate segmentation of tumor tissue is a key step during therapy and treatment planning. Since it is difficult to obtain a comprehensive set of tumor data of children, there is no benchmark so far allowing evaluation of the quality of human or computer-based segmentations. The contributions in our paper are threefold: (i) we present the first heterogeneous Wilms' tumor benchmark data set. It contains multisequence MRI data sets before and after chemotherapy, along with ground truth annotation, approximated based on the consensus of five human experts. (ii) We analyze human expert annotations and interrater varia…
Estimating feature discriminant power in decision tree classifiers
1995
Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.
Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach.
2016
We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the cor…
A Morphological Clustering Method for daily solar radiation curves
2011
Abstract We present a new method based on Mathematical Morphology techniques for the classification of solar radiation curves that we call MfCM. The main advantage of using MfCM as opposed to daily clearness index distributions is that it allows us to keep the dynamics of the solar radiance curves in the analysis: both cloud transitions and variability in direct radiation are simultaneously taken into account. To illustrate our proposal, we use a set of real radiation data collected in a location sited in southern Spain.
ADT implementation and completion by induction from examples
1991
There exists a fast algorithm [2] for inductive synthesis of terminating and ground confluent term rewriting systems from samples. The principles of this algorithm and the methodology of its use for implementation and completion of abstract data types are described.
Inductive synthesis of term rewriting systems
2005
Fast algorithm for inductive synthesis of term rewriting systems is described and proved to be correct. It is implemented and successfully applied for inductive synthesis of different algorithms, including the binary multiplication. The algorithm proposed supports automatic learning process and can be used for designing and implementation of ADT.
Using PageRank for non-personalized default rankings in dynamic markets
2017
Abstract Default ranking algorithms are used to generate non-personalized product rankings for standard consumers, for example, on landing pages of online stores. Default rankings are created without any information about the consumers’ preferences. This paper proposes using the product centrality ranking algorithm (PCRA), which solves some problems of existing default ranking algorithms: Existing approaches either have low accuracy, because they rely on only one product attribute, or they are unable to estimate ranks for new or updated products, because they use past consumer behavior, such as previous sales or ratings. The PCRA uses the PageRank centrality of products in a product dominat…
A GRASP heuristic for the mixed Chinese postman problem
2002
Abstract Arc routing problems (ARPs) consist of finding a traversal on a graph satisfying some conditions related to the links of the graph. In the Chinese postman problem (CPP) the aim is to find a minimum cost tour (closed walk) traversing all the links of the graph at least once. Both the Undirected CPP, where all the links are edges that can be traversed in both ways, and the Directed CPP, where all the links are arcs that must be traversed in a specified way, are known to be polynomially solvable. However, if we deal with a mixed graph (having edges and arcs), the problem turns out to be NP -hard. In this paper, we present a heuristic algorithm for this problem, the so-called Mixed CPP…