Input-Output HMM's for Sequence Processing  

Oleh:

Frasconi, P. ; Bengio, Y.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 5 (1996), page 1231-1249.
Topik: SEQUENCE ANALYSIS-METHODS; input - output; HMM; sequence processing

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We consider problems of sequence processing and propose a solution based on a discrete - state model in order to represent past context. We introduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state. The model has a statistical interpretation we call input - output hidden Markov model (IOHMM). It can be trained by the estimation - maximization (EM) or generalized EM (GEM) algorithms, considering state trajectories as missing data, which decouples temporal credit assignment and actual parameter estimation. The model presents similarities to hidden Markov models (HMM s), but allows us to map input sequences to output sequences, using the same processing style as recurrent neural networks. IOHMM s are trained using a more discriminant learning paradigm than HMM s, while potentially taking advantage of the EM algorithm. We demonstrate that IOHMM s are well suited for solving grammatical inference problems on a benchmark problem. Experimental results are presented for the seven Tomita grammars, showing that these adaptive models can attain excellent generalization.

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