Browsing by Author "Lopes, Noel"
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- High-performance bankruptcy prediction model using Graphics Processing UnitsPublication . Ribeiro, Bernardete; Lopes, Noel; Silva, CatarinaIn recent years the the potential and programmability of Graphics Processing Units (GPU) has raised a note-worthy interest in the research community for applications that demand high-computational power. In particular, in financial applications containing thousands of high-dimensional samples, machine learning techniques such as neural networks are often used. One of their main limitations is that the learning phase can be extremely consuming due to the long training times required which constitute a hard bottleneck for their use in practice. Thus their implementation in graphics hardware is highly desirable as a way to speed up the training process. In this paper we present a bankruptcy prediction model based on the parallel implementation of the Multiple BackPropagation (MBP) algorithm which is tested on a real data set of French companies (healthy and bankrupt). Results by running the MBP algorithm in a sequential processing CPU version and in a parallel GPU implementation show reduced computational costs with respect to the latter while yielding very competitive performance.
- Improving recall values in breast cancer diagnosis with Incremental Background KnowledgePublication . Silva, Catarina; Ribeiro, Bernardete; Lopes, NoelCancer diagnosis is generally the process of using some form of physical or genetic tests or exams, usually referred as patient data, to detect the disease. One of the main problems with cancer diagnosis systems is the lack of labeled data, as well as the difficulties of labeling pre-existing unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in cancer diagnosis. The possible availability of this kind of data for some applications makes it an appealing source of information. In this work we explore an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to better aid decisions, namely by improving recall values. The defined incremental SVM margin-based method was tested in the Wisconsin-Madison breast cancer diagnosis problem to examine the effectiveness of such techniques in supporting diagnosis.
- Learning the hash code with generalised regression neural networks for handwritten signature biometric data retrievalPublication . Ribeiro, Bernardete; Lopes, Noel; Silva, CatarinaHandwritten signature recognition is one important component of biometric authentication. This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions. Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information. Although many successful methods have been used, they often disregard the size of databases, which can be very large, posing scalability problems to their application in real-world scenarios. To overcome this problem, in this paper, we use binary embeddings of high-dimensional data which is an efficient tool for indexing big datasets of biometric images. The rationale is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the resulting Hamming space for fast data retrieval and state-of-the-art classification performance. In the settings of an handwritten signature retrieval system, an indexing hashing-based scheme is presented. We propose to learn k-bits hash code with a generalised regression neural network (GRNN), which yielded competitive results in the GPDS database.