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1
Вероятностное тематическое моделирование.. теория регуляризации АРТМ и библиотека с открытым кодом BigART.
Воронцов К.В.
модели
θtd
φwt
темы
модель
слов
topic
norm
документов
коллекции
ptdw
моделей
распределения
ndw
тематических
lda
nwt
термов
шага
матрицы
регуляризатор
conference
artm
поиска
ntd
регуляризации
тематического
число
документа
моделирования
тематические
данных
models
proceedings
алгоритм
вероятности
θd
распределение
рис
каждой
acm
задача
задачи
тематической
тема
документ
параметров
тематическая
αt
φvt
Anno:
2023
Lingua:
russian
File:
PDF, 2.33 MB
I tuoi tag:
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/
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russian, 2023
2
Analysis of Images, Social Networks and Texts: Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers
Springer International Publishing
Dmitry I. Ignatov
,
Mikhail Yu. Khachay
,
Alexander Panchenko
,
Natalia Konstantinova
,
Rostislav E. Yavorsky
,
(eds.)
russian
analysis
algorithm
method
approach
first
extraction
russia
topic
features
accuracy
models
methods
proceedings
sentence
dependencies
conference
query
database
dataset
users
springer
computational
step
processing
values
classification
semantic
proposed
detection
different
context
dictionary
topics
chants
exercises
feature
prediction
experiments
formal
function
systems
linguistics
examples
moscow
generated
association
contains
presented
parameter
Anno:
2014
Lingua:
english
File:
PDF, 27.71 MB
I tuoi tag:
0
/
0
english, 2014
3
Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy
MDPI
Sergei Koltcov
,
Vera Ignatenko
,
Zeyd Boukhers
,
Steffen Staab
entropy
topics
topic
renyi
parameters
models
regularization
dataset
lda
values
sparse
plsa
bigartm
hyper
likelihood
optimal
perplexity
approach
modeling
lenta
hdp
datasets
dirichlet
documents
proceedings
conference
figure
algorithm
s.k
vlda
matrix
theta
analysis
dependence
inference
latent
newsgroups
experiments
gibbs
method
variational
allows
determine
orange
parameter
authors
march
obtained
olive
probabilities
Anno:
2020
Lingua:
english
File:
PDF, 1.30 MB
I tuoi tag:
0
/
0
english, 2020
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