The phrase "empirical induction" is correct and commonly used in written English.
It refers to the process of making generalizations or conclusions based on specific observations or experiences. It is commonly used in scientific research and discussions. Example: The scientist used empirical induction to formulate a theory about the relationship between temperature and the rate of chemical reactions.
In mechanics, the categorical tools are the principle of (empirical) induction and differential equations.
Gödel makes two further observations: first, one can avoid the above difficulty by founding consistency on empirical induction.
This involved sharing in Hume's critique of abstract rationalist systems, but it also involved the very different project of defending empirical induction and experimental reasoning as the new epistemology appropriate for a modern Enlightened philosophy.
From these elementary phenomena, we generalize (e.g., via the empirical induction principle) to elementary facts and move, by means of differential equations, to laws and verifiable hypotheses whose number should be kept as small as possible (Poincaré 1902: 168 171; 168 171168 171).
The principles of mechanics certainly have, according to Poincaré, an empirical origin, but they nonetheless surpass the bounds of strict empiricism since the experience that leads us to an experimental result rests on the repetition of a phenomenon and therefore requires physical or empirical induction (Poincaré 1902: 26; 1913b: 30).
All these empirical inductions from the exploratory analysis of the component planes seem correct looking at the plotted data in Fig. 11, and highlight the visualization strengths of UbiSOM with streaming data.
Verifiable hypotheses employ conventional elements in the generalization process and may presuppose the (empirical) principle of induction.
In this context we presuppose the categories of empirical or physical induction and of differential equations as necessary preconditions for doing science.
These findings have been achieved without turning to empirical experiments involving inductions of classifiers (which have previously proved the usefulness of the DDP for both artificial and real-life datasets), thus establishing the fundamental underpinnings for the DDP concept.
Empirical refinement by correlational induction results in ever better observation-level generalizations about the world.
