A machine learning algorithm is an algorithm that’s ready to learn from data. But what can we mean by learning? Mitchell ( 1997 ) provides the definition “A computer virus is claimed to find out from experience E with reference to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” One can imagine a really big variety of experiences E, tasks T, and performance measures P, This post provides intuitive descriptions and samples of the various sorts of tasks, performance measures, and experiences that will be wont to construct machine learning algorithms.
Machine learning allows us to tackle tasks that are too difficult to unravel with fixed programs written and designed by the citizenry. From a scientific and philosophical point of view, machine learning is interesting because developing our understanding of machine learning entails developing our understanding of the
principles that underlie intelligence.
In this relatively formal definition of the word “task,” the method of learning itself isn’t the task. Learning is our means of achieving the power to perform the task. for instance, if we would like a robot to be ready to walk, then walking is that the task. We could program the robot to find out to steer, or we could plan to directly write
a program that specifies the way to walk manually. Many sorts of tasks are often solved with machine learning. a number of the foremost common machine learning tasks include the following:
- Classification: during this sort of task, the pc program is asked to specify which of k categories some input belongs to. to unravel this task, the training algorithm is typically asked to supply a function f: R n → {1, . . . , k}. When y = f (x), the model assigns an input described by vector x to a category identified by numeric code y. There are other variants of the classification task, for instance, where f outputs a probability distribution over classes. An example of a classification task is visual perception, where the input is a picture (usually described as a group of pixel brightness values), and therefore the output may be a numeric code identifying the thing within the image. for instance, the Willow Garage PR2 robot is in a position to act as a waiter which will recognize different sorts of drinks and deliver them to people on command (Good-fellow et al., 2010 ). Modern visual perception is best accomplished with deep learning ( Krizhevsky et al., 2012; Ioffe and Szegedy, 2015 ). visual perception is that the same basic technology that permits computers to acknowledge faces (Taigman et al., 2014 ), which may be wont to automatically tag people in photo collections and permit computers to interact more naturally with their users.
- Classification with missing inputs: Classification becomes tougher if the pc program isn’t guaranteed that each measurement in its input vector will always be provided. so as to unravel the classification task, the training algorithm only has got to define one function mapping from a vector input to a categorical output. When a number of the inputs could also be missing, instead of providing one classification function, the training algorithm must learn a group of functions. Each function corresponds to classifying x with a special subset of its inputs missing. this type of situation arises frequently in diagnosis because many sorts of medical tests are expensive or invasive. a method to efficiently define such an outsized set of functions is to find out a probability distribution over all of the relevant variables, then solve the classification task by marginalizing out the missing variables. With n input variables, we will now obtain all 2 n different classification functions needed for each possible set of missing inputs, but we only got to learn one function describing the probability distribution. See Goodfellow et al. ( 2013b ) for an example of a deep probabilistic model applied to such a task in this way. Many of the opposite tasks described during this section also can be generalized to figure with missing inputs; classification with missing inputs is simply one example of what machine learning can do.
- Regression: during this sort of task, the pc program is asked to predict a numerical value given some input. to unravel this task, the training algorithm is asked to output a function f: R n → R. this sort of task is analogous to classification, except that the format of the output is different. An example of a regression task is that the prediction of the expected claim amount that an insured will make (used to line insurance premiums), or the prediction of future prices of securities. These sorts of predictions also are used for algorithmic trading.
- Transcription: during this sort of task, the machine learning system is asked to watch a comparatively unstructured representation of some quiet data and transcribe it into discrete, textual form. for instance, in optical character recognition, the pc program is shown a photograph containing a picture of text and is asked to return this text within the sort of a sequence of characters (e.g., in ASCII or Unicode format). Google Street View uses deep learning to process address numbers in this way (Goodfellow et al., 2014d). Another example is speech recognition, where the pc program is provided an audio waveform and emits a sequence of characters or word ID codes describing the words that were spoken within the sound recording. Deep learning may be a crucial component of recent speech recognition systems used at major companies including Microsoft, IBM, and Google (Hinton et al.,2012b).
- Machine translation: during an MT task, the input already consists of a sequence of symbols in some language, and therefore the computer virus must convert this into a sequence of symbols in another language. this is often commonly applied to natural languages, like translating from English to French. Deep learning has recently begun to possess a crucial impact on this type of task (Sutskever et al., 2014; Bahdanau et al., 2015 ).
- Structured output: Structured output tasks involve any task where the output may be a vector (or other arrangement containing multiple values) with important relationships between the various elements. this is often a broad category and subsumes the transcription and translation tasks described above, but also many other tasks. One example is parsing—mapping a tongue sentence into a tree that describes its grammatical structure and tagging nodes of the trees as being verbs, nouns, or adverbs, and so on. See Collobert ( 2011 ) for an example of deep learning applied to a parsing task. Another example is pixel-wise segmentation of images, where the pc program assigns every pixel in a picture to a selected category. for instance, deep learning is often wont to annotate the locations of roads in aerial photographs (Mnih and Hinton , 2010 ). The output needn’t have its form mirror the structure of the input as closely as in these annotation-style tasks. for instance, in image captioning, the pc program observes a picture and outputs a tongue sentence describing the image ( Kiros et al., 2014a, b; Mao et al., 2015; Vinyals et al., 2015b; Donahue et al., 2014; Karpathy and Li, 2015; Fang et al., 2015; Xu et al., 2015 ). These tasks are called structured output tasks because the program must output several values that are all tightly interrelated. for instance, the words produced by a picture captioning the program must form a legitimate sentence.
- Anomaly detection: during this sort of task, the pc program sifts through a group of events or objects, and flags a number of them as being unusual or atypical. An example of an anomaly detection task is MasterCard fraud detection. By modeling your purchasing habits, a Mastercard company can detect misuse of your cards. If a thief steals your Mastercard or MasterCard information, the thief’s purchases will often come from a special probability distribution over purchase types than your own. The Mastercard company can prevent fraud by placing a hold on an account as soon as that card has been used for an uncharacteristic purchase. See Chandola et al. ( 2009 ) for a survey of anomaly detection methods.
- Synthesis and sampling: during this sort of task, the machine learning algorithm is asked to get new examples that are almost like those within the training data. Synthesis and sampling via machine learning are often useful for media applications where it is often expensive or boring for an artist to get large volumes of content by hand. for instance, video games can automatically generate textures for giant objects or landscapes, instead of requiring an artist to manually label each pixel ( Luo et al., 2013 ). In some cases, we want the sampling or synthesis procedure to get some specific quiet output given the input. for instance, during a speech synthesis task, we offer a written sentence and ask the program to emit an audio waveform containing a spoken version of that sentence. this is often a sort of structured output task, but with the added qualification that there’s no single correct output for every input, and that we explicitly desire an outsized amount of variation within the output, so as for the output to look more natural and realistic.
- Imputation of missing values: during this sort of task, the machine learning algorithm is given a replacement example x ∈ R n, but with some entries x i of x missing. The algorithm must provide a prediction of the values of the missing entries.
- Denoising: during this sort of task, the machine learning algorithm is given in input a corrupted example x̃ ∈ R n obtained by an unknown corruption process from a clean example x ∈ R n. The learner must predict the clean example x from its corrupted version x̃, or more generally predict the contingent probability distribution p(x | x̃).
- Density estimation or probability mass function estimation: within the density estimation problem, the machine learning algorithm is asked to find out a function p: R n → R, where p (x) is often interpreted as a probability density function (if is continuous) or a probability mass function (if is discrete) on the space that the examples were drawn from. to try to do such a task well (we will specify exactly what meaning once we discuss performance measures P ), the algorithm must learn the structure of the info it’s seen. It must know where examples cluster tightly and where they’re unlikely to occur. Most of the tasks described above require that the learning algorithm has a minimum of implicitly capturing the structure of the probability distribution. Density estimation allows us to explicitly capture that distribution. in theory, we will then perform computations thereon distribution so as to unravel the opposite tasks also. for instance, if we’ve performed density estimation to get a probability distribution p(x), we will use that distribution to unravel the missing value imputation task. If a value x i is missing and every one of the opposite values, denoted x i, is given, then we all know the distribution over it’s given by p(x i | x i ). In practice, density estimation doesn’t always allow us to unravel all of those related tasks because in many cases the specified operations on p( x) are computationally intractable. Of course, many other tasks and kinds of tasks are possible. the kinds of tasks we list here are intended only to supply samples of what machine learning can do, to not define a rigid taxonomy of tasks.
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