Search results for "Grammar induction"
showing 7 items of 7 documents
Application of the Error Correcting Grammatical Inference Method (ECGI) to Multi-Speaker Isolated Word Recognition
1988
It is well known that speech signals constitute highly structured objects which are composed of different kinds of subobjects such as words, phonemes, etc. This fact has motivated several researchers to propose different models which more or less explicitly assume the structural nature of speech. Notable examples of these models are Markov models /Bak 75/, /Jel 76/; the famous Harpy /Low 76/; Scriber and Lafs /Kla 80/; and many others works in which the convenience of some structural model of the speech objects considered is explicitly claimed /Gup 82/, /Lev 83/, /Cra 84/, /Sca 85/, /Kam 85/, /Sau 85/, /Rab 85/, /Kop 85/, /Sch 85/, /Der 86/, /Tan 86/.
Structural Knowledge Extraction from Mobility Data
2016
Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will …
Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces
2019
AbstractRecognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of su…
Extracting Structured Knowledge From Sensor Data for Hybrid Simulation
2014
Obtaining continuous and detailed monitoring of indoor environments has today become viable, also thanks to the widespread availability of effective and flexible sensing technology; this paves the way for the design of practical Ambient Intelligence systems, and for their actual deployment in real-life contexts, which require advanced functionalities, such as for instance the automatic discovery of the activities carried on by users. Novel issues arise in this context; on one hand, it is important to reliably model the phenomena under observation even though, to this end, it is often necessary to craft a carefully designed prototype in order to test and fine-tune the theoretical models.
A Structural Approach to Infer Recurrent Relations in Data
2014
Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word "knowledge" refers to the non trivial new relations not easily deducible from the observation of the data. Several approaches have been used to accomplish this task, ranging from statistical to structural methods, often heavily dependent on the particular problem of interest. In this work we propose a system for knowledge extraction that exploits the power of an ontology approach. Ontology is used to describe, organise and discover new knowledge. To show the effectiveness of our system in extracting and generalising the knowledge emb…
Gl-learning
2016
In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application s…
Learning the structure of HMM's through grammatical inference techniques
2002
A technique is described in which all the components of a hidden Markov model are learnt from training speech data. The structure or topology of the model (i.e. the number of states and the actual transitions) is obtained by means of an error-correcting grammatical inference algorithm (ECGI). This structure is then reduced by using an appropriate state pruning criterion. The statistical parameters that are associated with the obtained topology are estimated from the same training data by means of the standard Baum-Welch algorithm. Experimental results showing the applicability of this technique to speech recognition are presented. >