Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods
نویسندگان
چکیده
منابع مشابه
Evaluation of two methods of manual and mechanized harvesting Echium Amoenum L. and determine the most appropriate method of harvesting
Borage herbs, wild herbs and is one year with significant economic value are highly regarded local market Due to the high economic value of borage any study to improve the quality of harvest and increase product seems necessary, Two Rahim Abad (GILAN) district in the province Eshkourat the highest level of production as the location of choice, and studies of the factors in determining the best ...
متن کاملEvaluation of two methods of manual and mechanized harvesting Echium Amoenum L. and determine the most appropriate method of harvesting
Borage herbs, wild herbs and is one year with significant economic value are highly regarded local market Due to the high economic value of borage any study to improve the quality of harvest and increase product seems necessary, Two Rahim Abad (GILAN) district in the province Eshkourat the highest level of production as the location of choice, and studies of the factors in determining the best ...
متن کاملon the comparison of keyword and semantic-context methods of learning new vocabulary meaning
the rationale behind the present study is that particular learning strategies produce more effective results when applied together. the present study tried to investigate the efficiency of the semantic-context strategy alone with a technique called, keyword method. to clarify the point, the current study seeked to find answer to the following question: are the keyword and semantic-context metho...
15 صفحه اولStatistical Methods Statistical Methods#computational Linguistics#machine Learning#stochastic Grammars
" Statistical methods " refers here specifically to statistical methods in computational linguistics. This represents a new body of practice in computational linguistics that has become standard over the last decade.
متن کاملMachine Learning and Statistical MAP Methods
For machine learning of an input-output function f from examples, we show it is possible to define an a priori probability density function on the hypothesis space to represent knowledge of the probability distribution of f , even when the hypothesis space H is large (i.e., nonparametric). This allows extension of maximum a posteriori (MAP) estimation methods nonparametric function estimation. ...
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ژورنال
عنوان ژورنال: International Journal of Forest Engineering
سال: 2020
ISSN: 1494-2119,1913-2220
DOI: 10.1080/14942119.2020.1820750