38 research outputs found
A novel prototype generation technique for handwriting digit recognition
No full textThe aim of this paper is to introduce a novel prototype generation technique for handwriting digit recognition. Prototype generation is approached as a two-stage process. The first stage uses an Adaptive Resonance Theory 1 (ART1) based algorithm to select an effective initial solution, while the second one executes a fine tuning designed to generate the best prototypes.
To this end, the second stage deals with an optimization problem, in which the objective function to be minimized is the cost function associated to the classification. A naive evolution strategy is used to generate the prototype set able to reduce classification time, without greatly affecting the accuracy. Moreover, as the ART1 based algorithm has incremental learning capability, the first stage is also useful for selecting the prototype set according to variations in handwriting style. The classification task is performed by the k-nearest neighbor classifier.
Experimental tests on the MNIST dataset demonstrated that our technique represents a good trade-off among accuracy, classification speed and robustness to handwriting style changes
Effect of cold storage and controlled atmosphere on fruit and oil quality of ‘Paranzana’ olives
No full textObjective of the work was to verify the effect of cold storage and controlled atmosphere on long term storage of olives in relation to quality of the fruit and of the extracted oil. ‘Peranzana’ olives with about 50% of the skin turned black, were stored in air at ambient temperature (approximately 15 to 20 °C), air at 5 °C, and 3% O2 in nitrogen at 5 °C, in two replicates for treatment. Initially, and after 7, 17, 25 and 31 days samples were taken, and oil extracted with a laboratory scale milling plant. Before milling, respiration rate, firmness, color, and weight loss, were determined on drupes from every replicate. On oil samples the following quality attributes were measured: acidity, peroxide value, coefficients of specific extinction at 232 and 270 nm, oil stability (Rancimat test), and total phenolics. Olive quality was strongly affected by temperature; olives stored in air at room temperature showed an higher metabolic activity as indicated by respiration rate, percentage of color development, higher weight loss and lower firmness, than olives stored at 5 °C (both in air and low oxygen). In addition olives stored at room temperature were affected by decay, starting from 17 days of storage, while no decay was observed during the whole duration of the storage at 5 °C. Temperature also affected olive oil quality, while little differences were observed between air and low oxygen conditions at 5 °C. Acidity, peroxide value and specific extinction coefficients in the oil obtained from olives stored in air at room temperature, were significantly higher than in the oil obtained from olives stored at 5 °C (both in air and low oxygen). Results showed a good potential of refrigeration for application in the olive oil industry, in order to preserve quality of the product and to lengthen the processing season.
Keywords. Olea europea L., olive oi
HFSP: Size-based Scheduling for Hadoop
No full textIn this paper, we present a size-based scheduling protocol for Hadoop, that caters to both interactivity and efficiency requirements raised by current Hadoop clusters.
Our scheduler addresses several challenges such as job size estimation, resource management and scheduling complex jobs with inter-related phases. Furthermore, by employing the technique of job aging, we avoid the problem of job starvation typical of well known size-based policies.
Our experiments pinpoint at a significant decrease in average job sojourn times -- a metric that accounts for the total time a job spends in the system, including waiting and serving times -- for realistic workloads generated according to production traces available in literature
INSTANCE SELECTION METHOD IN MULTI-EXPERT SYSTEM FOR ONLINE SIGNATURE VERIFICATION
No full textIn real world applications, signature verification systems should be able to learn continuously, as new signatures providing additional information become available. In fact, new data are not equally relevant for system improvement and a suitable data filtering strategy is generally required. In this context, instance selection is an important task for signature verification systems in order to select useful signatures to be considered for updating system knowledge, removing irrelevant and/or redundant instances from new data.
This paper proposes a new feedback-based learning strategy to update the knowledge-base in multi-expert signature verification system. In particular, the collective behavior of classifiers is considered to select the samples for updating system knowledge. Evaluation tests provide a comparison between our (not naïve) approach and the traditional approach, which uses the entire new dataset for feedback. For the purpose, two state-of-the-art classifiers (NB and k-NN) and two abstract level combination techniques (MV and WMV) were used. The experimental results, carried out considering the SUSig database, demonstrate the effectiveness of the new strategy
Learning Iterative Strategies in Multi-Expert Systems using SVMs for Digit Recognition
No full textThis paper presents three different learning iterative strategies, in a
multi-expert system. In first strategy entire new dataset is used. In second strategy
each single classifier selects new samples starting from those on which it
performs a misclassification. Finally, the collective behavior of classifiers is
studied to select the most profitable samples for knowledge base updating. The
experimental results provide a comparison of three approaches under different
operating conditions and feedback process. A classifier SVM and four different
combination techniques were used by considering the CEDAR (handwritten
digit) database. It is shown how results depend by the iterations on the feedback
process, as well as by the specific combination decision schema and by data
distribution
Voronoi Tessellation for Effective and Efficient Handwritten Digit Classification
No full textThe aim of this paper is to explore the
properties of a new zoning technique based on Voronoi
tessellation for the task of handwritten digit recognition.
This technique extracts features according to an optimal
zoning distribution, obtained by an evolutionary-strategy
based search. Extensive experiments have been conducted
on the MNIST dataset to investigate strengths and
weakness of the proposed approach. Comparisons with
regular square zoning reveal that the presented zoning
strategy achieves better results with any type of features.
Furthermore, the proposed zoning method, jointly with a
suitable choice of features, allows a low complexity
classifier to reach excellent performances both in terms of
accuracy and speed
In vitro and in vivo evaluation of polyoxyethylene esters as dermal prodrugs of chetoprofen, naproxen and diclofenac
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