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Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together...
Semi-Supervised Support Vector Machines (S3VMs) have been proposed to deal with the proliferation of partially labelled data available in many large-scale complex systems. Since most existing S3VMs do not correspond to convex problems nor have practical solutions especially on large imbalanced data, this limits their utility in practice. In this work, we propose an efficient approach to the semi-supervised...
There is very little practicable significance to prove the equivalency between a pseudo-inverse linear discriminant (PILD) with the desired outputs in reverse proportion to the number of within-class samples and a Fisher linear discriminant (FLD) with the totally projected mean thresholds which are disadvantageous to improve the overall classification accuracy. Even if so, several examples have borne...
We develop T2API, a context-sensitive, graph-based statisticaltranslation approach that takes as input an English description of aprogramming task and synthesizes the corresponding API code templatefor the task. We train T2API to statistically learn the alignmentsbetween English and APIs and determine the relevant API elements. Thetraining is done on StackOverflow, which is a bilingual corpus onwhich...
This paper proposes a classification approach in which monolithic and multiple classifier systems are combined in a cascading fashion. The rationale behind that is to deal with the existing trade-off between the need for increasing the accuracy, while reducing the complexity of the classification method. In other words, the idea is to offer an interesting strategy to conciliate the different levels...
The Oracle model has been used not only for comparison between techniques but also in the design of different methods in Multiple Classifier Systems (MCS). Even though the model represents the ideal classifier selection scheme, Dynamic Classifier Selection (DCS) techniques present a large performance gap from the Oracle. This means that, for a significant number of instances, the DCS techniques are...
A biological neural network is constituted by numerous subnetworks and modules with different functionalities. For an artificial neural network, the relationship between a network and its subnetworks is also important and useful for both theoretical and algorithmic research, i.e. it can be exploited to develop incremental network training algorithm or parallel network training algorithm. In this paper...
Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, and it can be applied to any base learner. In this paper, we propose a weighted-resampling method for transfer learning, called TrResampling. Firstly, resampling is applied to the data...
The method presented extends a given regression neural network to make its performance improve. The modification affects the learning procedure only, hence the extension may be easily omitted during evaluation without any change in prediction. It means that the modified model may be evaluated as quickly as the original one but tends to perform better. This improvement is possible because the modification...
Previous RNN architectures have largely been superseded by LSTM, or “Long Short-Term Memory”. Since its introduction, there have been many variations on this simple design. However, it is still widely used and we are not aware of a gated-RNN architecture that outperforms LSTM in a broad sense while still being as simple and efficient. In this paper we propose a modified LSTM-like architecture. Our...
The aim of the paper is to explore how models based on a linear dynamic can be used in order to perform a prediction task in sequential domains. In the literature, it has already been shown that Linear Dynamical Systems (LDSs) can be quite useful when dealing with sequence learning tasks. Our aim is to study whether it is possible to use LDSs as building blocks for constructing more complex and powerful...
In this paper, a TV logo detection system is proposed based on the deep learning architecture for the specific TV logo detection task. Training a robust object detector typically requires a large amount of manually annotated data, which is time-consuming. To reduce the cost, we construct a TV logo detection system in a weakly-supervised framework, which is accomplished by a TV logo localization network...
Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images...
Automatic image captioning has received increasing attention in recent years. Although there are many English datasets developed for this problem, there is only one Turkish dataset and it is very small compared to its English counterparts. Creating a new dataset for image captioning is a very costly and time consuming task. This work is a first step towards transferring the available, large English...
Scaling up Artificial Intelligence (AI) algorithms for massive datasets to improve their performance is becoming crucial. In Machine Translation (MT), one of most important research fields of AI, models based on Recurrent Neural Net- works (RNN) show state-of-the-art performance in recent years, and many researchers keep working on improving RNN-based models to achieve better accuracy in translation...
Recently, the multi-label learning has drawn considerable attention as it has many applications in text classification, image annotation and query/keyword suggestions etc. In recent years, a number of remedies have been proposed to address this challenging task. However, they are either tree based methods which has the expensive train costs or embedding based methods which has relatively lower accuracy...
In order to be considered as Linked Data, the datasets on the web must be linked to other datasets. Current studies on dataset interlinking prediction researches do not distinguish the type of links, which are of less help for real application scenarios, as dataset publishers still do not know what kinds of RDF links can be established and furthermore how to configure the data linking algorithms....
The need for skilled arthroscopic surgeons is increasing due to the large number of arthroscopic interventions performed annually. Surgical simulators are beneficial training platforms for practicing those difficult to learn surgical tasks. In this study, a sensorized physical shoulder simulator was developed. This simulator incorporates switch sensors for objective assessment of probing tasks and...
Hashing technique has become an effective method for information retrieval due to the fast calculation of the Hamming distance. However, with the continuous growth of data coming from the Internet, the online update of hashing on the massive social data becomes very time-consuming. To alleviate this issue, in this paper, we propose a novel updating technique for hashing methods, namely Hamming Subspace...
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