Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

Earth and Planetary Sciences

• Approach 100%
• Biomass 25%
• Classifier 25%
• Community 25%
• Comparison 25%
• Datum 25%
• Decision Making 25%
• Environmental Condition 50%
• Exploration 25%
• Fish 100%
• Fisheries Management 50%
• Fishery 25%
• Generalisation 25%
• Goodness of Fit 25%
• Horse 100%
• Improvement 50%
• Information 25%
• Investigation 25%
• Machine Learning 50%
• Management 75%
• Medium 25%
• Model 25%
• Probability Theory 25%
• Purpose 25%
• Recovery Plan 25%
• Reliability 25%
• Spawning 50%
• Species 50%
• Subject 25%
• Supervised Classification 25%

Social Sciences

• Biomass 25%
• Classification 25%
• Climatic Data 25%
• Communities 25%
• Comparison 25%
• Decision Making Management 25%
• Example 25%
• Forecasting Model 25%
• Information 25%
• Learning 25%
• Machines 50%
• Management 50%
• Methodology 25%
• Probability 25%
• Purpose 25%
• Recruitment 100%
• Reliability 25%
• Scenarios 50%
• Species 50%
• Statistical Inference 75%
• Subject 25%

Agricultural and Biological Sciences

• Albacore 25%
• Anchovies 25%
• Biomass 25%
• Blue Whiting 25%
• Classification 25%
• Decision Making 25%
• Estimation 75%
• Fish 50%
• Fisheries Management 25%
• Hake 25%
• Horses 50%
• Information 25%
• Learning 50%
• Mackerel 75%
• Methodology 25%
• Probability 25%
• Reliability 25%
• Sardine 25%
• Spawning 50%
• Uncertainty 75%