A well-posed learning problem is defined as one in which computer programs’ performance on a specific task improves with experience, such as machine learning models for handwriting recognition that will enhance their accuracy over time. For instance, machine learning models for handwriting recognition could qualify as well-posed learning problems if their accuracy increases with training experience.
Problems that aren’t adequately posed may be mathematically unstable. For instance, continuous nonlinear problems that lack good conditions could become unstable over time, such that small fluctuations in initial data could cause much more significant changes in answers than intended.
A well-posed learning problem is defined as any machine learning task that can be solved iteratively by applying the same algorithm to different inputs without resorting to additional training experiences (E). A well-posed learning problem often has three components: task T, performance measure P, and training experience E. Computer programs can learn from experience E in regard to task T when their performance on it improves with more training experiences. For instance, an email program may become better at categorizing spam emails if it repeatedly classifies certain emails as spam or non-spam.
Well-posed problems depend on solutions that rely only on continuous changes to input parameters or data and have unique solutions at every value of each relevant data point. Furthermore, their answers should be stable enough that minor errors in input will not cause significant variations in output results; the inverse heat equation does not fit this description since its answers depend on initial conditions for answering it correctly.
Another critical characteristic of a well-posed problem is its solve-ability with computational methods that can be repeated without incurring errors; for instance, ordinary least squares are often used to find vector x that minimizes functions between x and y even when there is plenty of statistical noise present in data sets. By contrast, poorly posed problems may require reformulation or regularization before computer algorithms can solve them successfully; this usually involves making assumptions that make solving it more accurate – Tikhonov regularization is one such popular technique used to solve linear discrete ill-posed linear discrete problems that violate one or more conditions outlined above.
An otherwise well-posed problem is considered ill-conditioned if its solution has a large condition number, defined as the minimum nonnegative number satisfying $Kabs = Krel$ for pair $(f,x)$. What constitutes a small or large condition number should be assessed individually according to each problem’s circumstances.
Tom Mitchell describes a well-posed learning problem as one that contains three components. These components include the following elements: T, P, and E – tasks T (the learning behavior being improved by computer), P (performance measure of task), and E (number of times the program completes task), with training experience as the goal: to increase knowledge on how to complete the job successfully by the computer program.
Well-posed problems have solutions (s) for every data point d relevant to their answer; each unique s exists for every d, and their dependence upon changes to d is continuous (i.e., tiny changes in d lead to proportionally more significant changes in s). This criteria, known as Hadamard criteria, provides an efficient means of judging whether or not a problem lends itself well to mathematical analysis – and many critical physical problems like advection equations, ultrasound imaging, and optimal control theory are well-posed problems requiring mathematical analysis, such as Advection Equations Advection Equations Advection Equations Ultrasound imaging and optimal control theory are well posed as well.
Problems that are poorly posed often require reframing before being solved using computational algorithms, usually by making new assumptions to define and restrict it more clearly. For instance, solving the inverse heat equation requires knowing how its initial temperature distribution was set up; without this knowledge, its solution might be overly sensitive to small changes in data – hence why such problems are considered “bad” scientifically.
Problems may also be well-posed but poorly constrained, which means a mistake in input data can result in significant errors in output. This type of situation often arises in complex nonlinear systems like chaotic ones.
Computer programs are said to learn from experience E with regard to task T and performance measure P when their performance at task T improves due to training experience E. For instance, an email program that watches how you categorize emails as spam or non-spam will adapt its abilities by learning from past decisions made by its users in classifying emails as spam or not spam more accurately in future emails received from you.
A well-posed problem has a solution that continuously depends on its input data or parameters; that is, small changes to one variable will produce proportionally significant changes to another variable. This characteristic allows mathematical analysis to be done on such problems.
When a problem does not meet all the conditions required of a well-posed problem, it is known as being “ill-posed.” This makes solving it much harder using algorithms; even the slightest error in input data could lead to significant inaccuracies in solutions.
An ill-posed problem includes, among others, solving first-order differential equations, matrix inversion, and inverse problems. Ill-posedness has long been studied as an application of applied mathematics; today, it remains an active area of research across many scientific fields. Even when problems do have solutions, they could still be classified as ill-posed due to violation of Hadamard criteria if those solutions could go on forever (violation of Hadamard criteria being one method). Regularization techniques provide accurate approximate solutions to such ill-posed issues.
There’s more than meets the eye with regards to this one – take a look! – because its success lies within. Well-posed learning problems -also called optimal learning problems- refer to computer programs that continuously improve on a task T, with respect to some performance measure P. For instance, an email program could improve its ability to categorize emails as spam or non-spam by studying your marking behavior and playing practice games against itself with the aim of winning a certain percentage of games against itself. Well-posed problems feature three elements, which include the learning task (T), performance measure (P), and training experience (E). Some examples include handwriting recognition systems, which improve their abilities by studying how you write before practicing against themselves; machine learning models may also qualify as well-posed when they learn checkers with performance measures that include how many games won against an opponent.
Even well-posed problems may be ill-conditioned, meaning that even minor differences in initial data may lead to significant discrepancies in simulation results – something familiar when dealing with models of nonlinear systems like chaotic ones.
Reformulation or the introduction of new assumptions is sometimes needed in order to make an ill-posed problem mathematically tractable and produce approximate solutions that make sense. A popular method for solving such ill-posed issues is Tikhonov regularization, which stabilizes these problems through prior information.