Abstract: A method of selecting a plant variety for cultivation in a target area includes selecting a selection score function; estimating values of a first set of environmental parameters for a predefined future period of time for the target area and receiving a set of phenotype information including phenotypic trait measurements for a first sub-set of a plurality of plant varieties and a set o environmental parameters for said first sub-set. Furthermore, the method includes determining a prediction model for the phenotypic traits; using the prediction model to output predictions for phenotypic traits for the plurality of chosen plant varieties; using the selection score function to compute selection score values; and selecting at least one plant variety to be cultivated in the target area from the plurality of chosen plant varieties, based the computed selection score values of the plurality of chosen plant varieties.

Abstract: A method of selecting a plant variety for cultivation in a target area includes selecting a selection score function; estimating values of a first set of environmental parameters for a predefined future period of time for the target area and receiving a set of phenotype information including phenotypic trait measurements for a first sub-set of a plurality of plant varieties and a set environmental parameters for said first sub-set. Furthermore, the method includes determining a prediction model for the phenotypic traits; using the prediction model to output predictions for phenotypic traits for the plurality of chosen plant varieties; using the selection score function to compute selection score values; and selecting at least one plant variety to be cultivated in the target area from the plurality of chosen plant varieties, based the computed selection score values of the plurality of chosen plant varieties.

Abstract: A general-purpose knowledge finding method for efficient knowledge finding by selectively sampling only data in large information amounts from a database. Learning means 104 causes a lower-order learning algorithm, inputted via an input unit 107, to perform learning on plural partial samples generated by sampling from data stored in a high-speed main memory 120, to obtain plural hypotheses. Data selection means 105 uses the hypotheses to estimate information amounts of respective candidate data read from a large-capacity data storage device 130, and additionally stores only data in large information amounts into the high-speed main memory 120. A control unit 106 repeats the processing a predetermined number of times, and stores obtained final hypotheses. A prediction unit 102 predicts a label value of unknown-labeled data inputted into the input unit 107 by the final hypotheses, and an output unit 101 outputs the predicted value.

Abstract: A general-purpose knowledge finding method for efficient knowledge finding by selectively sampling only data in large information amounts from a database. Learning means 104 causes a lower-order learning algorithm, inputted via an input unit 107, to perform learning on plural partial samples generated by sampling from data stored in a high-speed main memory 120, to obtain plural hypotheses. Data selection means 105 uses the hypotheses to estimate information amounts of respective candidate data read from a large-capacity data storage device 130, and additionally stores only data in large information amounts into the high-speed main memory 120. A control unit 106 repeats the processing a predetermined number of times, and stores obtained final hypotheses. A prediction unit 102 predicts a label value of unknown-labeled data inputted into the input unit 107 by the final hypotheses, and an output unit 101 outputs the predicted value.