Essays about: "parameterskattning"
Showing result 1 - 5 of 11 essays containing the word parameterskattning.
-
1. Optimizing Search Engine Field Weights with Limited Data : Offline exploration of optimal field weight combinations through regression analysis
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Modern search engines, particularly those utilizing the BM25 ranking algorithm, offer a multitude of tunable parameters designed to refine search results. Among these parameters, the weight of each searchable field plays a crucial role in enhancing search outcomes. READ MORE
-
2. Orbit-simulator for downstream processes
University essay from Lunds universitet/Kemiteknik (CI)Abstract : In an ever more digitilized society, the transition is palpable also in the pharmaceutical industry. The control system Orbit was developed at the department of chemical engineering at Lund University, to be able to automate downstream processes based on the ÄKTA-system. READ MORE
-
3. Joint Estimation and Calibration for Motion Sensor
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In the thesis, a calibration method for positions of each accelerometer in an Inertial Sensor Array (IMU) sensor array is designed and implemented. In order to model the motion of the sensor array in the real world, we build up a state space model. Based on the model we use, the problem is to estimate the parameters within the state space model. READ MORE
-
4. Jump Estimation of Hidden Markov Models with Time-Varying Transition Probabilities
University essay from Lunds universitet/Matematisk statistikAbstract : The Hidden Markov model is applicable to a wide variety of fields. Applied to financial time series, its assumed underlying state sequence can reflect the time series' tendency to behave differently over different periods of time. In many situations, models could be improved by including exogenous data. READ MORE
-
5. Rating corrumption within insurance companies using Bayesian network classifiers
University essay from Umeå universitet/StatistikAbstract : Bayesian Network (BN) classifiers are a type of probabilistic models. The learning process consists of two steps, structure learning and parameter learning. Four BN classifiers will be learned. These are two different Naive Bayes classifiers (NB), one Tree Augmented Naive Bayes classifier (TAN) and one Forest Naive Bayes classifier (FAN). READ MORE