Mar 14, 2020 12:58
4 yrs ago
10 viewers *
English term
21 books translated
Native in: Russian 
Works in: English to Russian, Russian to English, Spanish to Russian, and 1 more.
Sequence learners
English to Russian
Tech/Engineering
Computers (general)
Доброго времени суток.
Из книги об использовании ИИ в маркетинге.
Alerting
We scan the Internet for signals and this way, we are informed about eco- nomic changes in companies. Any mention of companies is analysed and the impact they have is evaluated and whether they reveal a positive or a negative development. A rapidly increasing number of complaints to a non- responsive customer service can be an indication of internal problems within the company. News, blogs, social media and the website are a highly topical source of information about the condition and development of a company. Scheduled relocations, structural changes, expansion strategies or profit announcements, for example, are quickly visible and are a sign of a positive or negative development of a company. On the basis of these “early signals”, statements can be made about how probable a company will react to being addressed at the current point in time.
Alerting openly scans the Internet and crawls cyclically websites and social media channels for content snippets containing information about a company. These snippets are the potential alerts that are filtered and aggregated according to significance down the line. In the first step, the probability of a company being mentioned in the given text is determined. ***Sequence learners***, which make a decision based on the lexical similarity and the context of the word as to whether the mention refers to a company or not, are used for this purpose.
In the second step, a deep learner decides whether the validated snippets on a company trigger an alert or whether they are a part of daily background noise. To this end, a model is trained on the basis of historic text data and corresponding share developments, to recognise correlations between snip- pets and the development on the stock market. The time lag between alert and real change “lag” is automatically learned by the system. Recurrent neural networks, in comparison with other approaches on the basis of a “sliding time window” in combination with a classical regression, do not have the limitation of the finite number of input values.
Спасибо.
Из книги об использовании ИИ в маркетинге.
Alerting
We scan the Internet for signals and this way, we are informed about eco- nomic changes in companies. Any mention of companies is analysed and the impact they have is evaluated and whether they reveal a positive or a negative development. A rapidly increasing number of complaints to a non- responsive customer service can be an indication of internal problems within the company. News, blogs, social media and the website are a highly topical source of information about the condition and development of a company. Scheduled relocations, structural changes, expansion strategies or profit announcements, for example, are quickly visible and are a sign of a positive or negative development of a company. On the basis of these “early signals”, statements can be made about how probable a company will react to being addressed at the current point in time.
Alerting openly scans the Internet and crawls cyclically websites and social media channels for content snippets containing information about a company. These snippets are the potential alerts that are filtered and aggregated according to significance down the line. In the first step, the probability of a company being mentioned in the given text is determined. ***Sequence learners***, which make a decision based on the lexical similarity and the context of the word as to whether the mention refers to a company or not, are used for this purpose.
In the second step, a deep learner decides whether the validated snippets on a company trigger an alert or whether they are a part of daily background noise. To this end, a model is trained on the basis of historic text data and corresponding share developments, to recognise correlations between snip- pets and the development on the stock market. The time lag between alert and real change “lag” is automatically learned by the system. Recurrent neural networks, in comparison with other approaches on the basis of a “sliding time window” in combination with a classical regression, do not have the limitation of the finite number of input values.
Спасибо.
Proposed translations
(Russian)
| 3 | последовательно (само)обучающиеся системы |
mk_lab
|
| 3 | изучатели/анализаторы последовательности |
Enote
|
Proposed translations
1 hr
Selected
последовательно (само)обучающиеся системы
(нейронно-сетевые) системы ИИ, основанные на алгоритме последовательного (само)обучения
Самообучающиеся системы и их применение для принятия решений
https://infourok.ru/samoobuchayuschiesya-sistemi-i-ih-primen...
Алгоритм обучения сети Кохонена включает этапы, состав которых зависит от типа структуры: постоянной (самообучающаяся сеть) или переменной (самоорганизующаяся сеть). Для самообучения последовательно выполняются:
1. Задание структуры сети (количества нейронов слоя Кохонена) (K).
2. Случайная инициализация весовых коэффициентов значениями, удовлетворяющими одному из следующих ограничений:
– при нормализации исходной выборки
--------------------------------------------------
Note added at 3 hrs (2020-03-14 16:56:19 GMT)
--------------------------------------------------
Нужно заметить, что learners (ученики) - это обучающиеся система, а никакие не "изучатели/анализаторы". Концепция ИИ заключается в том, что нейронные сети сами ничего не изучают и не анализируют (это дело обычных традиционных систем анализа - без ИИ). Они адаптируются (изменяют свою структуру и узловые коэффициенты) путем самообучения, чтобы давать правильные результаты по последовательности данных
Самообучающиеся системы и их применение для принятия решений
https://infourok.ru/samoobuchayuschiesya-sistemi-i-ih-primen...
Алгоритм обучения сети Кохонена включает этапы, состав которых зависит от типа структуры: постоянной (самообучающаяся сеть) или переменной (самоорганизующаяся сеть). Для самообучения последовательно выполняются:
1. Задание структуры сети (количества нейронов слоя Кохонена) (K).
2. Случайная инициализация весовых коэффициентов значениями, удовлетворяющими одному из следующих ограничений:
– при нормализации исходной выборки
--------------------------------------------------
Note added at 3 hrs (2020-03-14 16:56:19 GMT)
--------------------------------------------------
Нужно заметить, что learners (ученики) - это обучающиеся система, а никакие не "изучатели/анализаторы". Концепция ИИ заключается в том, что нейронные сети сами ничего не изучают и не анализируют (это дело обычных традиционных систем анализа - без ИИ). Они адаптируются (изменяют свою структуру и узловые коэффициенты) путем самообучения, чтобы давать правильные результаты по последовательности данных
4 KudoZ points awarded for this answer.
Comment: "Большое спасибо всем.
Спасибо, mk_lab."
2 hrs
изучатели/анализаторы последовательности
Изучение или обучение по последовательности исходных элементов - это одна из концепций ИИ, вот цитата из https://devblogs.nvidia.com/deep-learning-nutshell-sequence-...
Sequence Learning
Everything in life depends on time and therefore, represents a sequence. To perform machine learning with sequential data (text, speech, video, etc.) we could use a regular neural network and feed it the entire sequence, but the input size of our data would be fixed, which is quite limiting. Other problems with this approach occur if important events in a sequence lie just outside of the input window. What we need is (1) a network to which we can feed sequences of arbitrary length one element of the sequence per time step (for example a video is just a sequence of images; we feed the network one image at a time); and (2) a network which has some kind of memory to remember important events which happened many time steps in the past. These problems and requirements have led to a variety of different recurrent neural networks.
Здесь, имхо, последовательностью является исходный текст, а элементами - слова. Это соответствует контексту: make a decision based on the lexical similarity and the context of the word
Sequence Learning
Everything in life depends on time and therefore, represents a sequence. To perform machine learning with sequential data (text, speech, video, etc.) we could use a regular neural network and feed it the entire sequence, but the input size of our data would be fixed, which is quite limiting. Other problems with this approach occur if important events in a sequence lie just outside of the input window. What we need is (1) a network to which we can feed sequences of arbitrary length one element of the sequence per time step (for example a video is just a sequence of images; we feed the network one image at a time); and (2) a network which has some kind of memory to remember important events which happened many time steps in the past. These problems and requirements have led to a variety of different recurrent neural networks.
Здесь, имхо, последовательностью является исходный текст, а элементами - слова. Это соответствует контексту: make a decision based on the lexical similarity and the context of the word
Something went wrong...