Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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01 Sep 2016
TL;DR: The primary goal of the model proposed in this paper is to predict airline delays caused by inclement weather conditions using data mining and supervised machine learning algorithms.
Abstract: The primary goal of the model proposed in this paper is to predict airline delays caused by inclement weather conditions using data mining and supervised machine learning algorithms. US domestic flight data and the weather data from 2005 to 2015 were extracted and used to train the model. To overcome the effects of imbalanced training data, sampling techniques are applied. Decision trees, random forest, the AdaBoost and the k-Nearest-Neighbors were implemented to build models which can predict delays of individual flights. Then, each of the algorithms' prediction accuracy and the receiver operating characteristic (ROC) curve were compared. In the prediction step, flight schedule and weather forecast were gathered and fed into the model. Using those data, the trained model performed a binary classification to predicted whether a scheduled flight will be delayed or on-time.
115 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Samples that are incorrectly predicted in the previous classifiers are chosen more often or weighted more heavily when estimating a new classifier [12]....
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...Then the label having the largest vote is assigned to the test point x [12]....
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01 Jun 2008
TL;DR: The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning, which combines the adoption of a Bayesian viewpoint, use of graphical models to represent complex probability distributions, and the development of fast, deterministic inference algorithms.
Abstract: The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning. The cornerstones of this approach are (i) the adoption of a Bayesian viewpoint, (ii) the use of graphical models to represent complex probability distributions, and (iii) the development of fast, deterministic inference algorithms, such as variational Bayes and expectation propagation, which provide efficient solutions to inference and learning problems in terms of local message passing algorithms. This paper reviews the key ideas behind this new framework, and highlights some of its major benefits. The framework is illustrated using an example large-scale application.
115 citations
Cites background from "Pattern Recognition and Machine Lea..."
...A much more detailed and comprehensive treatment of the topics discussed here, including additional references, can be found in [5]....
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TL;DR: This work proposes a straightforward linear Gaussian approach, where decoding relies on the inversion of properly regularized encoding models, which can still be solved analytically, and is shown to yield state-of-the-art reconstructions of perceived characters as estimated from BOLD responses.
115 citations
Cites background from "Pattern Recognition and Machine Lea..."
...(7) is a standard result obtained in Bayesian linear regression (Bishop, 2006)....
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TL;DR: A sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches is proposed, and its practical potential is shown by successfully evaluating its generalization capabilities across both domain and sensor modalities.
115 citations
Cites result from "Pattern Recognition and Machine Lea..."
...While this approach is in line with the standard procedures in many application domains of general pattern recognition and machine learning techniques [12], it is often too costly or simply not applicable for ubiquitous/pervasive computing applications....
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TL;DR: In this article, an RNN is trained by using as features the angles formed by the finger bones of the human hands, acquired by a leap motion controller sensor, and the proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American sign language.
Abstract: Hand gesture recognition is still a topic of great interest for the computer vision community. In particular, sign language and semaphoric hand gestures are two foremost areas of interest due to their importance in human–human communication and human–computer interaction, respectively. Any hand gesture can be represented by sets of feature vectors that change over time. Recurrent neural networks (RNNs) are suited to analyze this type of set thanks to their ability to model the long-term contextual information of temporal sequences. In this paper, an RNN is trained by using as features the angles formed by the finger bones of the human hands. The selected features, acquired by a leap motion controller sensor, are chosen because the majority of human hand gestures produce joint movements that generate truly characteristic corners. The proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American sign language. On the latter, an accuracy of over 96% was achieved. Afterwards, by using the Shape Retrieval Contest (SHREC) dataset, a wide collection of semaphoric hand gestures, the method was also proven to outperform in accuracy competing approaches of the current literature.
115 citations