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The effectiveness of the divided recitation supervised study period in Kansas high schoolsGoforth, Ernest Constant January 2011 (has links)
Typescript, etc. / Digitized by Kansas State University Libraries
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Perceptrons in kernel feature spacesFriess, Thilo-Thomas January 2000 (has links)
No description available.
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Calibrating recurrent sliding window classifiers for sequential supervised learningJoshi, Saket Subhash 03 October 2003 (has links)
Sequential supervised learning problems involve assigning a class label to
each item in a sequence. Examples include part-of-speech tagging and text-to-speech
mapping. A very general-purpose strategy for solving such problems is
to construct a recurrent sliding window (RSW) classifier, which maps some window
of the input sequence plus some number of previously-predicted items into
a prediction for the next item in the sequence. This paper describes a general purpose
implementation of RSW classifiers and discusses the highly practical
issue of how to choose the size of the input window and the number of previous
predictions to incorporate. Experiments on two real-world domains show that
the optimal choices vary from one learning algorithm to another. They also
depend on the evaluation criterion (number of correctly-predicted items versus
number of correctly-predicted whole sequences). We conclude that window
sizes must be chosen by cross-validation. The results have implications for the
choice of window sizes for other models including hidden Markov models and
conditional random fields. / Graduation date: 2004
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The relationship of selected variables to the supervision provided students of vocational agriculture by their teachers /Byers, Charles W. January 1972 (has links)
No description available.
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Unsupervised Learning of Spatiotemporal Features by Video CompletionNallabolu, Adithya Reddy 18 October 2017 (has links)
In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per-pixel reconstruction loss. To achieve good reconstruction results using color videos, the CNN needs to have a certain level of understanding of the scene dynamics and predict plausible, temporally coherent contents. We further explore to jointly reconstruct both color frames and flow fields. By exploiting the statistical temporal structure of images, we show that the learned representations capture meaningful spatiotemporal structures from raw videos. We validate the effectiveness of our approach for CNN pre-training on action recognition and action similarity labeling problems. Our quantitative results demonstrate that our method compares favorably against learning without external data and existing unsupervised learning approaches. / Master of Science
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On surrogate supervision multi-view learningJin, Gaole 03 December 2012 (has links)
Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision multi-view learning, where labels are available on limited views and a classifier is obtained on the target view where labels are missing. In surrogate multi-view learning, one cannot obtain a classifier without information from the auxiliary view. To solve this challenging problem, we propose discriminative and generative approaches. / Graduation date: 2013
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Částečně řízené učení algoritmů strojového učení (semi-supervised learning)Burda, Karel January 2014 (has links)
The final thesis summarizes in its theoretical part basic knowledge of machine learning algorithms that involves supervised, semi-supervised, and unsupervised learning. Experiments with textual data in natural spoken language involving different machine learning methods and parameterization are carried out in its practical part. Conclusions made in the thesis may be of use to individuals that are at least slightly interested in this domain.
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Development of physics-based reduced-order models for reacting flow applications / Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactifAversano, Gianmarco 15 November 2019 (has links)
L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution temporelle (DNS2). Des méthodes telles que l’analyse en composantes principales (ACP), l’analyse en composantes principales locales (LPCA), la factorisation matricielle non négative (NMF) et les autoencodeurs ont été explorées à cette fin. Il a été démontré que divers facteurs pouvaient affecter les performances de ces méthodes, tels que les critères utilisés pour le centrage et la mise à l’échelle des données d’origine ou le choix du nombre de dimensions dans les approximations de rang inférieur. Un ensemble de lignes directrices a été présenté qui peut aider le processus d’identification de caractéristiques physiques significatives à partir de données de flux réactifs turbulents. Des méthodes de compression de données telles que l’analyse en composantes principales (ACP) et les variations ont été combinées à des méthodes d’interpolation telles que le krigeage, pour la construction de modèles ordonnées à prix réduits et calculables pour la prédiction de l’état d’un système de combustion dans des conditions de fonctionnement inconnues ou des combinaisons de modèles valeurs de paramètre d’entrée. La méthodologie a d’abord été testée pour la prévision des flammes 1D avec un nombre croissant de paramètres d’entrée (rapport d’équivalence, composition du carburant et température d’entrée), avec des variantes de l’approche PCA classique, à savoir PCA contrainte et PCA locale, appliquée aux cas de combustion la première fois en combinaison avec une technique d’interpolation. Les résultats positifs de l’étude ont conduit à l’application de la méthodologie proposée aux flammes 2D avec deux paramètres d’entrée, à savoir la composition du combustible et la vitesse d’entrée, qui ont donné des résultats satisfaisants. Des alternatives aux méthodes non supervisées et supervisées choisies ont également été testées sur les mêmes données 2D. L’utilisation de la factorisation matricielle non négative (FNM) pour l’approximation de bas rang a été étudiée en raison de la capacité de la méthode à représenter des données à valeur positive, ce qui permet de ne pas enfreindre des lois physiques importantes telles que la positivité des fractions de masse d’espèces chimiques et comparée à la PCA. Comme méthodes supervisées alternatives, la combinaison de l’expansion du chaos polynomial (PCE) et du Kriging et l’utilisation de réseaux de neurones artificiels (RNA) ont été testées. Les résultats des travaux susmentionnés ont ouvert la voie au développement d’un jumeau numérique d’un four à combustion à partir d’un ensemble de simulations 3D. La combinaison de PCA et de Kriging a également été utilisée dans le contexte de la quantification de l’incertitude (UQ), en particulier dans le cadre de collaboration de données lié (B2B-DC), qui a conduit à l’introduction de la procédure B2B-DC à commande réduite. Comme pour la première fois, le centre de distribution B2B a été développé en termes de variables latentes et non en termes de variables physiques originales. / With the final objective being to developreduced-order models for combustion applications,unsupervised and supervised machine learningtechniques were tested and combined in the workof the present Thesis for feature extraction and theconstruction of reduced-order models. Thus, the applicationof data-driven techniques for the detection offeatures from turbulent combustion data sets (directnumerical simulation) was investigated on two H2/COflames: a spatially-evolving (DNS1) and a temporallyevolvingjet (DNS2). Methods such as Principal ComponentAnalysis (PCA), Local Principal ComponentAnalysis (LPCA), Non-negative Matrix Factorization(NMF) and Autoencoders were explored for this purpose.It was shown that various factors could affectthe performance of these methods, such as the criteriaemployed for the centering and the scaling of theoriginal data or the choice of the number of dimensionsin the low-rank approximations. A set of guidelineswas presented that can aid the process ofidentifying meaningful physical features from turbulentreactive flows data. Data compression methods suchas Principal Component Analysis (PCA) and variationswere combined with interpolation methods suchas Kriging, for the construction of computationally affordablereduced-order models for the prediction ofthe state of a combustion system for unseen operatingconditions or combinations of model input parametervalues. The methodology was first tested forthe prediction of 1D flames with an increasing numberof input parameters (equivalence ratio, fuel compositionand inlet temperature), with variations of the classicPCA approach, namely constrained PCA and localPCA, being applied to combustion cases for the firsttime in combination with an interpolation technique.The positive outcome of the study led to the applicationof the proposed methodology to 2D flames withtwo input parameters, namely fuel composition andinlet velocity, which produced satisfactory results. Alternativesto the chosen unsupervised and supervisedmethods were also tested on the same 2D data.The use of non-negative matrix factorization (NMF) forlow-rank approximation was investigated because ofthe ability of the method to represent positive-valueddata, which helps the non-violation of important physicallaws such as positivity of chemical species massfractions, and compared to PCA. As alternative supervisedmethods, the combination of polynomial chaosexpansion (PCE) and Kriging and the use of artificialneural networks (ANNs) were tested. Results from thementioned work paved the way for the developmentof a digital twin of a combustion furnace from a setof 3D simulations. The combination of PCA and Krigingwas also employed in the context of uncertaintyquantification (UQ), specifically in the bound-to-bounddata collaboration framework (B2B-DC), which led tothe introduction of the reduced-order B2B-DC procedureas for the first time the B2B-DC was developedin terms of latent variables and not in terms of originalphysical variables.
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An investigation of supervised learning in genetic programmingGathercole, Christopher January 1998 (has links)
This thesis is an investigation into Supervised Learning (SL) in Genetic Programming (GP). With its flexible tree-structured representation, GP is a type of Genetic Algorithm, using the Darwinian idea of natural selection and genetic recombination, evolving populations of solutions over many generations to solve problems. SL is a common approach in Machine Learning where the problem is presented as a set of examples. A good or fit solution is one which can successfully deal with all of the examples. In common with most Machine Learning approaches, GP has been used to solve many trivial problems. When applied to larger and more complex problems, however, several difficulties become apparent. When focusing on the basic features of GP, this thesis highlights the immense size of the GP search space, and describes an approach to measure this space. A stupendously flexible but frustratingly useless representation, Anarchically Automatically Defined Functions, is described. Some difficulties associated with the normal use of the GP operator Crossover (perhaps the most common method of combining GP trees to produce new trees) are demonstrated in the simple MAX problem. Crossover can lead to irreversible sub-optimal GP performance when used in combination with a restriction on tree size. There is a brief study of tournament selection which is a common method of selecting fit individuals from a GP population to act as parents in the construction of the next generation. The main contributions of this thesis however are two approaches for avoiding the fitness evaluation bottleneck resulting from the use of SL in GP. to establish the capability of a GP individual using SL, it must be tested or evaluated against each example in the set of training examples.
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Shrunken learning rates do not improve AdaBoost on benchmark datasetsForrest, Daniel L. K. 30 November 2001 (has links)
Recent work has shown that AdaBoost can be viewed as an algorithm that
maximizes the margin on the training data via functional gradient descent. Under
this interpretation, the weight computed by AdaBoost, for each hypothesis generated,
can be viewed as a step size parameter in a gradient descent search. Friedman
has suggested that shrinking these step sizes could produce improved generalization
and reduce overfitting. In a series of experiments, he showed that very small
step sizes did indeed reduce overfitting and improve generalization for three variants
of Gradient_Boost, his generic functional gradient descent algorithm. For this
report, we tested whether reduced learning rates can also improve generalization in
AdaBoost. We tested AdaBoost (applied to C4.5 decision trees) with reduced learning
rates on 28 benchmark datasets. The results show that reduced learning rates
provide no statistically significant improvement on these datasets. We conclude that
reduced learning rates cannot be recommended for use with boosted decision trees
on datasets similar to these benchmark datasets. / Graduation date: 2002
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