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Aha, David W.; Kibler, Dennis; Albert, Marc K.

doi:10.1023/a:1022689900470

Cited by 1,063 publications

(51 citation statements)

References 38 publications

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“…K-nearest neighbors (kNN) classifier calculates distance between feature vector of input vector and others feature vectors from training set [40]. It has used Euclidean distance metrics, k = 3 and distance weighting with equation: 1/distance.…”

Section: Features Extractionmentioning

confidence: 99%

Mouth features extraction for emotion classiﬁcation

Robert

Wojciechowski

2016

Annals of Computer Science and Information Systems

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Abstract-Face emotions analysis is one of the fundamental techniques that might be exploited in a natural human-computer interaction process and thus is one of the most studied topics in current computer vision literature. In consequence face features extraction is an indispensable element of the face emotion analysis as it influences decision making performance. The paper concentrates on classification of human poses based on mouth. Mouth features extraction, which next to eye region features becomes one of the most representative face regions in the context of emotions retrieval. Additionally, in the paper, original mouth features extraction method was presented. It is gradient based. Evaluation of the method was performed for a subset of the Yale images database and classification accuracy for single emotion is over 70%.

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Section: Features Extractionmentioning

confidence: 99%

Mouth features extraction for emotion classiﬁcation

Robert

Wojciechowski

2016

Annals of Computer Science and Information Systems

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“…Relational Instance-Based Learning (RIBL) algorithms extend the idea of instance based learning to relational learning (Emde and Wettschereck, 1996). Instance-Based Learning (IBL) algorithms (Aha et al, 1991) are very popular and a well studied choice (Wettsschereck and Dietterich, 1995) for propositional learning problems. Probabilistic Relational Models (PRMs) (Getoor et al, 2001) provide another approach to relational data mining that is grounded in a sound statistical framework.…”

Section: Learning Data In a Multi-relational Environmentmentioning

confidence: 99%

BioDARA: Data Summarization Approach to Extracting Bio-Medical Structuring Information

Kheau¹

2011

Journal of Computer Science

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Problem statement: Due to the ever growing amount of biomedical datasets stored in multiple tables, Information Extraction (IE) from these datasets is increasingly recognized as one of the crucial technologies in bioinformatics. However, for IE to be practically applicable, adaptability of a system is crucial, considering extremely diverse demands in biomedical IE application. One should be able to extract a set of hidden patterns from these biomedical datasets at low cost. Approach: In this study, a new method is proposed, called Bio-medical Data Aggregation for Relational Attributes (BioDARA), for automatic structuring information extraction for biomedical datasets. BioDARA summarizes biomedical data stored in multiple tables in order to facilitate data modeling efforts in a multi-relational setting. BioDARA has the advantages or capabilities to transform biomedical data stored in multiple tables or databases into a Vector Space model, summarize biomedical data using the Information Retrieval theory and finally extract frequent patterns that describe the characteristics of these biomedical datasets. Results: the results show that data summarization performed by DARA, can be beneficial in summarizing biomedical datasets in a complex multi-relational environment, in which biomedical datasets are stored in a multi-level of one-to-many relationships and also in the case of datasets stored in more than one one-to-many relationships with non-target tables. Conclusion: This study concludes that data summarization performed by BioDARA, can be beneficial in summarizing biomedical datasets in a complex multi-relational environment, in which biomedical datasets are stored in a multi-level of one-to-many relationships.

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“…IBK is an instance based algorithm (Aha et al, 1991), which belongs to the lazy learning algorithms, using the k-Nearest Neighbors algorithm (k-NN). At first it stores the training instances verbatim and then searches for the instance that most closely resembles the new instance.…”

Section: Lazy Learning Algorithmsmentioning

confidence: 99%

Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

Lazaridis¹

2010

Journal of Computer Science

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Problem statement:In this study we cope with the task of phone duration modeling for Greek emotional speech synthesis. Approach: Various well established machine learning techniques are applied for this purpose to an emotional speech database consisting of five archetypal emotions. The constructed phone duration prediction models are built on phonetic, morphosyntactic and prosodic features that can be extracted only from text. We employ model and regression trees, linear regression, lazy learning algorithms and meta-learning algorithms using regression trees as base classifiers, trained on a Modern Greek emotional database consisting of five emotional categories: anger, fear, joy, neutral and sadness. Results: Model trees based on the M5' algorithm and meta-learning algorithms using as base classifier regression trees based on the M5' algorithm proved to perform better. Conclusion: It was observed that the emotional categories of the speech database with the most uniform distribution of phone durations built the most accurate models.

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Untitled

Cited by 1,063 publications

References 38 publications

Mouth features extraction for emotion classiﬁcation

Mouth features extraction for emotion classiﬁcation

BioDARA: Data Summarization Approach to Extracting Bio-Medical Structuring Information

Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

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