501 research outputs found
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in
robot vision, their ability is often limited to closed world scenarios, where
the number of semantic concepts to be recognized is determined by the available
training set. Since it is practically impossible to capture all possible
semantic concepts present in the real world in a single training set, we need
to break the closed world assumption, equipping our robot with the capability
to act in an open world. To provide such ability, a robot vision system should
be able to (i) identify whether an instance does not belong to the set of known
categories (i.e. open set recognition), and (ii) extend its knowledge to learn
new classes over time (i.e. incremental learning). In this work, we show how we
can boost the performance of deep open world recognition algorithms by means of
a new loss formulation enforcing a global to local clustering of class-specific
features. In particular, a first loss term, i.e. global clustering, forces the
network to map samples closer to the class centroid they belong to while the
second one, local clustering, shapes the representation space in such a way
that samples of the same class get closer in the representation space while
pushing away neighbours belonging to other classes. Moreover, we propose a
strategy to learn class-specific rejection thresholds, instead of heuristically
estimating a single global threshold, as in previous works. Experiments on
RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202
Machine Learning with a Reject Option: A survey
Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with rejection recently gained interest. This
machine learning subfield enables machine learning models to abstain from
making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with rejection.
We introduce the conditions leading to two types of rejection, ambiguity and
novelty rejection, which we carefully formalize. Moreover, we review and
categorize strategies to evaluate a model's predictive and rejective quality.
Additionally, we define the existing architectures for models with rejection
and describe the standard techniques for learning such models. Finally, we
provide examples of relevant application domains and show how machine learning
with rejection relates to other machine learning research areas
Automotive Interior Sensing - Anomaly Detection
Com o surgimento dos veículos autónomos partilhados não haverá condutores nos veículos capazes de manter o bem-estar dos passageiros. Por esta razão, é imperativo que exista um sistema preparado para detetar comportamentos anómalos, por exemplo, violência entre passageiros, e que responda de forma adequada. O tipo de anomalias pode ser tão diverso que ter um "dataset" para treino que contenha todas as anomalias possíveis neste contexto é impraticável, implicando que algoritmos tradicionais de classificação não sejam ideais para esta aplicação. Por estas razões, os algoritmos de deteção de anomalias são a melhor opção para construir um bom modelo discriminativo.
Esta dissertação foca-se na utilização de técnicas de "deep learning", mais precisamente arquiteturas baseadas em "Spatiotemporal auto-encoders" que são treinadas apenas com sequências de "frames" de comportamentos normais e testadas com sequências normais e anómalas dos "datasets" internos da Bosch. O modelo foi treinado inicialmente com apenas uma categoria das ações não violentas e as iterações finais foram treinadas com todas as categorias de ações não violentas. A rede neuronal contém camadas convolucionais dedicadas à compressão e descompressão dos dados espaciais; e algumas camadas dedicadas à compressão e descompressão temporal dos dados, implementadas com células LSTM ("Long Short-Term Memory") convolucionais, que extraem informações relativas aos movimentos dos passageiros. A rede define como reconstruir corretamente as sequências de "frames" normais e durante os testes, cada sequência é classificada como normal ou anómala de acordo com o seu erro de reconstrução. Através dos erros de reconstrução são calculados os "regularity scores" que indicam a regularidade que o modelo previu para cada "frame". A "framework" resultante é uma adição viável aos algoritmos tradicionais de reconhecimento de ações visto que pode funcionar como um sistema que serve para detetar ações desconhecidas e contribuir para entender o significado de tais interações humanas.With the appearance of SAVs (Shared Autonomous Vehicles) there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviours, e.g., violence between passengers, and trigger the appropriate response. Furthermore, the type of anomalous activities can be so diverse, that having a dataset that incorporates most use cases is unattainable, making traditional classification algorithms not ideal for this kind of application. In this sense, anomaly detection algorithms are a good approach in order to build a discriminative model.
Taking this into account, this work focuses on the use of deep learning techniques, more precisely Spatiotemporal auto-encoder based frameworks, which are trained on human behavior video sequences and tested on use cases with normal and abnormal human interactions from Bosch's internal datasets. Initially, the model was trained on a single non-violent action category. Final iterations considered all of the identified non-violent actions as normal data. The network architecture presents a group of convolutional layers which encode and decode spatial data; and a temporal encoder/decoder structure, implemented as a convolutional Long Short Term Memory network, responsible for learning motion information. The network defines how to properly reconstruct the 'normal' frame sequences and during testing, each sequence is classified as normal or abnormal based on its reconstruction error. Based on these values, regularity scores are inferred showing the predicted regularity of each frame. The resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/abnormal behaviours and aid in understanding the meaning of such human interactions
Machine learning for Internet of Things data analysis: A survey
Rapid developments in hardware, software, and communication technologies have
allowed the emergence of Internet-connected sensory devices that provide
observation and data measurement from the physical world. By 2020, it is
estimated that the total number of Internet-connected devices being used will
be between 25 and 50 billion. As the numbers grow and technologies become more
mature, the volume of data published will increase. Internet-connected devices
technology, referred to as Internet of Things (IoT), continues to extend the
current Internet by providing connectivity and interaction between the physical
and cyber worlds. In addition to increased volume, the IoT generates Big Data
characterized by velocity in terms of time and location dependency, with a
variety of multiple modalities and varying data quality. Intelligent processing
and analysis of this Big Data is the key to developing smart IoT applications.
This article assesses the different machine learning methods that deal with the
challenges in IoT data by considering smart cities as the main use case. The
key contribution of this study is presentation of a taxonomy of machine
learning algorithms explaining how different techniques are applied to the data
in order to extract higher level information. The potential and challenges of
machine learning for IoT data analytics will also be discussed. A use case of
applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is
presented for a more detailed exploration.Comment: Digital Communications and Networks (2017
Machine Learning for Internet of Things Data Analysis: A Survey
Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration
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