6 research outputs found
Missing Value Imputation With Unsupervised Backpropagation
Many data mining and data analysis techniques operate on dense matrices or
complete tables of data. Real-world data sets, however, often contain unknown
values. Even many classification algorithms that are designed to operate with
missing values still exhibit deteriorated accuracy. One approach to handling
missing values is to fill in (impute) the missing values. In this paper, we
present a technique for unsupervised learning called Unsupervised
Backpropagation (UBP), which trains a multi-layer perceptron to fit to the
manifold sampled by a set of observed point-vectors. We evaluate UBP with the
task of imputing missing values in datasets, and show that UBP is able to
predict missing values with significantly lower sum-squared error than other
collaborative filtering and imputation techniques. We also demonstrate with 24
datasets and 9 supervised learning algorithms that classification accuracy is
usually higher when randomly-withheld values are imputed using UBP, rather than
with other methods
Recommending Learning Algorithms and Their Associated Hyperparameters
The success of machine learning on a given task dependson, among other
things, which learning algorithm is selected and its associated
hyperparameters. Selecting an appropriate learning algorithm and setting its
hyperparameters for a given data set can be a challenging task, especially for
users who are not experts in machine learning. Previous work has examined using
meta-features to predict which learning algorithm and hyperparameters should be
used. However, choosing a set of meta-features that are predictive of algorithm
performance is difficult. Here, we propose to apply collaborative filtering
techniques to learning algorithm and hyperparameter selection, and find that
doing so avoids determining which meta-features to use and outperforms
traditional meta-learning approaches in many cases.Comment: Short paper--2 pages, 2 table
A Continuous Space Generative Model
Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one model for both steps, such that learning can transfer between both operations. Also, the continuous images that CSGM generates may be sampled at arbitrary resolutions, opening the way for new possibilities with generative models. This paper presents results obtained by training on the MNIST dataset of handwritten digits to validate the method, and it elaborates on the potential applications for continuous generative models
Information technologies: science, engineering, technology, education, health. Part 4
ΠΠΎΠ΄Π°Π½ΠΎ ΡΠ΅Π·ΠΈ Π΄ΠΎΠΏΠΎΠ²ΡΠ΄Π΅ΠΉ Π½Π°ΡΠΊΠΎΠ²ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΎΡ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΡΡ MicroCAD-2017 Π·Π° ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ½ΠΈΠΌΠΈ ΡΠ° ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΌΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π½Π°ΡΠΊΠΎΠ²ΠΈΡ
Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΎΠΊ, ΡΠΊΡ Π²ΠΈΠΊΠΎΠ½Π°Π½Ρ Π²ΠΈΠΊΠ»Π°Π΄Π°ΡΠ°ΠΌΠΈ Π²ΠΈΡΠΎΡ ΡΠΊΠΎΠ»ΠΈ, Π½Π°ΡΠΊΠΎΠ²ΠΈΠΌΠΈ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ, Π°ΡΠΏΡΡΠ°Π½ΡΠ°ΠΌΠΈ, ΡΡΡΠ΄Π΅Π½ΡΠ°ΠΌΠΈ, ΡΠ°Ρ
ΡΠ²ΡΡΠΌΠΈ ΡΡΠ·Π½ΠΈΡ
ΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΡΠΉ Ρ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ². ΠΠ»Ρ Π²ΠΈΠΊΠ»Π°Π΄Π°ΡΡΠ², Π½Π°ΡΠΊΠΎΠ²ΠΈΡ
ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ², Π°ΡΠΏΡΡΠ°Π½ΡΡΠ², ΡΡΡΠ΄Π΅Π½ΡΡΠ², ΡΠ°Ρ
ΡΠ²ΡΡΠ². Π’Π΅Π·ΠΈ Π΄ΠΎΠΏΠΎΠ²ΡΠ΄Π΅ΠΉ Π²ΡΠ΄ΡΠ²ΠΎΡΠ΅Π½Ρ Π· Π°Π²ΡΠΎΡΡΡΠΊΠΈΡ
ΠΎΡΠΈΠ³ΡΠ½Π°Π»ΡΠ²
Information technologies: science, engineering, technology, education, health. Part 4
ΠΠΎΠ΄Π°Π½ΠΎ ΡΠ΅Π·ΠΈ Π΄ΠΎΠΏΠΎΠ²ΡΠ΄Π΅ΠΉ Π½Π°ΡΠΊΠΎΠ²ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΎΡ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΡΡ MicroCAD-2017 Π·Π° ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ½ΠΈΠΌΠΈ ΡΠ° ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΌΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π½Π°ΡΠΊΠΎΠ²ΠΈΡ
Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΎΠΊ, ΡΠΊΡ Π²ΠΈΠΊΠΎΠ½Π°Π½Ρ Π²ΠΈΠΊΠ»Π°Π΄Π°ΡΠ°ΠΌΠΈ Π²ΠΈΡΠΎΡ ΡΠΊΠΎΠ»ΠΈ, Π½Π°ΡΠΊΠΎΠ²ΠΈΠΌΠΈ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ, Π°ΡΠΏΡΡΠ°Π½ΡΠ°ΠΌΠΈ, ΡΡΡΠ΄Π΅Π½ΡΠ°ΠΌΠΈ, ΡΠ°Ρ
ΡΠ²ΡΡΠΌΠΈ ΡΡΠ·Π½ΠΈΡ
ΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΡΠΉ Ρ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ². ΠΠ»Ρ Π²ΠΈΠΊΠ»Π°Π΄Π°ΡΡΠ², Π½Π°ΡΠΊΠΎΠ²ΠΈΡ
ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ², Π°ΡΠΏΡΡΠ°Π½ΡΡΠ², ΡΡΡΠ΄Π΅Π½ΡΡΠ², ΡΠ°Ρ
ΡΠ²ΡΡΠ². Π’Π΅Π·ΠΈ Π΄ΠΎΠΏΠΎΠ²ΡΠ΄Π΅ΠΉ Π²ΡΠ΄ΡΠ²ΠΎΡΠ΅Π½Ρ Π· Π°Π²ΡΠΎΡΡΡΠΊΠΈΡ
ΠΎΡΠΈΠ³ΡΠ½Π°Π»ΡΠ²