1,721,106 research outputs found
Preface
No full textThe Preface presents the rationale of the book on conflict and provides the outline of the papers. An exploration of theoretical models defining conflict and negotiation, and overviewing technology to detect, predict and understand social cues, in order to analyze and prevent conflict
Estimating the intrinsic dimension of data with a fractal-based method
Full text linkIn this paper, the problem of estimating the intrinsic dimension of a data set is investigated. A fractal-based approach using the Grassberger-Procaccia algorithm is proposed. Since the Grassberger-Procaccia algorithm (1983) performs badly on sets of high dimensionality, an empirical procedure that improves the original algorithm has been developed. The procedure has been tested on data sets of known dimensionality and on time series of Santa Fe competition
Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm
No full textIn this paper the problem of estimating the intrinsic dimension of a data set is investigated. An approach based on the Grassberger–Procaccia's algorithm has been studied. Since this algorithm does not yield accurate measures in high-dimensional data sets, an empirical procedure has been developed. Grassberger–Procaccia's algorithm was tested on two different benchmarks and was compared to a TRN-based method
Machine Learning for Image, Video and Audio Analysis
No full textABSTRACT
Machine Learning involves several scientific domains including mathematics, computer science, statistics and biology, and is an approach that enables computers to automatically learn from data. Focusing on complex media and how to convert raw data into useful information, this book offers both introductory and advanced material in the combined fields of machine learning and image/video processing.
The machine learning techniques presented enable readers to address many real world problems involving complex data. Examples covering areas such as automatic speech and handwriting transcription, automatic face recognition, and semantic video segmentation are included, along with detailed introductions to algorithms and examples of their applications.
The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning tec..
Cursive Character Recognition by Learning Vector Quantization
No full textThis paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: The first one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49,000 isolated characters. © 2001 Elsevier Science B.V. All rights reserved
Spotting the traces of depression in read speech: An approach based on computational paralinguistics and social signal processing
No full textThis work investigates the use of a classification approach as a means to identify effective depression markers in read speech, i.e., observable and measurable traces of the pathology in the way people read a predefined text. This is important because the diagnosis of depression is still a challenging problem and reliable markers can, at least to a partial extent, contribute to address it. The experiments have involved 110 individuals and revolve around the tendency of depressed people to read slower and display silences that are both longer and more frequent. The results show that features expected to capture such differences reduce the error rate of a baseline classifier by more than 50% (from 31.8% to 15.5%). This is of particular interest when considering that the new features are less than 10% of the original set (3 out of 32). Furthermore, the results appear to be in line with the findings of neuroscience about brain-level differences between depressed and non-depressed individuals
Social Signals. From Theory to Applications.
No full textThe Special Issue Editorial introduces the research milieu in which Social Signal Processing originates, by merging Computer Scientists and Social Scientists and giving rise to this field in parallel with Human-Computer Interaction, Affective Computing, and Embodied Conversational Agents, all similarly characterized by high interdisciplinarity, stress on multimodality of communication, and the continuous loop from theory to simulation and application. Some frameworks of the cognitive and social processes underlying social signals are identified as reference points (Theory of Mind and Intersubjectivity, mirror neurons and the ontogenesis and phylogenesis of communication), while three dichotomies (automatic vs. controlled, individualistic vs. intersubjective, and meaning vs. influence) are singled out as leads to navigate within the theoretical and applicative studies presented in the Special Issue
THIN SLICES OF DEPRESSION: IMPROVING DEPRESSION DETECTION PERFORMANCE THROUGH DATA SEGMENTATION
No full textThe computing community is making major efforts towards automatic detection of depression, a serious pathology that affects roughly 4.4% of the world's population. One of the main difficulties is the collection of data aimed at training models capable to learn differences between depressed and nondepressed people. In fact, data collection in the depression domain requires the respect of rigorous ethical constraints that, inevitably, limit the size of the corpora that can be collected. This article proposes to address the problem by using the thin slices theory, i.e., the possibility to detect the inner state of an individual (depression in this case) through very short samples of behavior. In particular, the article shows that the performance of data-driven models can be improved by segmenting the data at disposition into thin slices and then training data-driven models over them. This increases the amount of samples at disposition and allows a relative F1 Score improvement by up to 16.2%
Combining neural gas and learning vector quantization for cursive character recognition
Full text linkThis paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach.
The character classification is achieved by combining the use of neural gas (NG) and learning vector quantization (LVQ). NG is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, it is possible to find an optimal number of classes maximizing the accuracy of the LVQ classifier.
A database of 58000 characters was used to train and test the models. The performance obtained is among the highest presented in the literature for the recognition of cursive characters
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