Sequence to Sequence Learning with Neural Networks, 2014 Likewise, question answering can also be seen as mapping a sequence of words representing the question to a sequence of words representing the answer. For example, speech recognition and machine translation are sequential problems. It is a significant limitation, since many important problems are best expressed with sequences whose lengths are not known a-priori. Given an image as input, generate a sequence of words that describe an image.Įxample of a Sequence-to-Sequence Prediction Problemĭespite their flexibility and power, can only be applied to problems whose inputs and targets can be sensibly encoded with vectors of fixed dimensionality. Sequence generation may also refer to the generation of a sequence given a single observation as input.Īn example is the automatic textual description of images. Given a corpus of examples of music, generate new musical pieces that have the properties of the corpus. Given a corpus of handwriting examples, generate handwriting for new phrases that has the properties of handwriting in the corpus. Given a corpus of text, such as the works of Shakespeare, generate new sentences or paragraphs of text that read like Shakespeare. Some examples of sequence generation problems include: Generating Sequences With Recurrent Neural Networks, 2013. In other words by making the network treat its inventions as if they were real, much like a person dreaming Assuming the predictions are probabilistic, novel sequences can be generated from a trained network by iteratively sampling from the network’s output distribution, then feeding in the sample as input at the next step. can be trained for sequence generation by processing real data sequences one step at a time and predicting what comes next. Sequence generation involves generating a new output sequence that has the same general characteristics as other sequences in the corpus. Given a sequence of text such as a review or a tweet, predict whether sentiment of the text is positive or negative. Given a sequence of observations, predict whether the sequence is anomalous or not.
Given a DNA sequence of ACGT values, predict whether the sequence codes for a coding or non-coding region. Some examples of sequence classification problems include: In the latter case, such problems may be referred to as discrete sequence classification. The input sequence may be comprised of real values or discrete values. Chapter 14, Data Classification: Algorithms and Applications, 2015 The objective of sequence classification is to build a classification model using a labeled dataset D so that the model can be used to predict the class label of an unseen sequence. Sequence classification involves predicting a class label for a given input sequence.Įxample of a Sequence Classification Problem Given a sequence of past purchases of a customer, predict the next purchase of a customer. Given a sequence of movements of a security over time, predict the next movement of the security. Given a sequence of observations about the weather over time, predict the expected weather tomorrow. Some examples of sequence prediction problems include: This can make things confusing for beginners.
Technically, we could refer to all of the following problems in this post as a type of sequence prediction problem. On Prediction Using Variable Order Markov Models, 2004. Applications involving sequential data may require prediction of new events, generation of new sequences, or decision making such as classification of sequences or sub-sequences. Learning of sequential data continues to be a fundamental task and a challenge in pattern recognition and machine learning. Sequence prediction may also generally be referred to as “ sequence learning“. CPT+: Decreasing the time/space complexity of the Compact Prediction Tree, 2015 This task has numerous applications such as web page prefetching, consumer product recommendation, weather forecasting and stock market prediction. A prediction consists in predicting the next items of a sequence. Once trained, the model is used to perform sequence predictions. Sequence Learning: From Recognition and Prediction to Sequential Decision Making, 2001.Ī prediction model is trained with a set of training sequences. Sequence prediction attempts to predict elements of a sequence on the basis of the preceding elements
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